Artificial intelligence-based information management system performance metric prediction

ABSTRACT

An information management system is disclosed herein that can use artificial intelligence to identify situations in which a performance metric may not be satisfied. For example, a storage manager of the information management system can maintain data related to historical, current, and/or future execution of secondary copy operations by secondary storage computing device(s) in the information management system. Using some or all of this data, the storage manager can train an artificial intelligence model (e.g., a neural network) to classify whether a current or future secondary copy operation job is likely to succeed or fail. Similarly, the storage manager can use some or all of this data to train another artificial intelligence model (e.g., a machine learning model) to predict the length of time for a current or future secondary copy operation job to complete. The trained models can be used to predict whether a performance metric will be satisfied.

INCORPORATION BY REFERENCE TO ANY PRIORITY APPLICATIONS

Any and all applications, if any, for which a foreign or domesticpriority claim is identified in the Application Data Sheet of thepresent application are hereby incorporated by reference in theirentireties under 37 CFR 1.57.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialwhich is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentand/or the patent disclosure as it appears in the United States Patentand Trademark Office patent file and/or records, but otherwise reservesall copyrights whatsoever.

BACKGROUND

Businesses recognize the commercial value of their data and seekreliable, cost-effective ways to protect the information stored on theircomputer networks while minimizing impact on productivity. A companymight back up critical computing systems such as databases, fileservers, web servers, virtual machines, and so on as part of a daily,weekly, or monthly maintenance schedule. The company may similarlyprotect computing systems used by its employees, such as those used byan accounting department, marketing department, engineering department,and so forth. Given the rapidly expanding volume of data undermanagement, companies also continue to seek innovative techniques formanaging data growth, for example by migrating data to lower-coststorage over time, reducing redundant data, pruning lower priority data,etc. Enterprises also increasingly view their stored data as a valuableasset and look for solutions that leverage their data. For instance,data analysis capabilities, information management, improved datapresentation and access features, and the like, are in increasingdemand.

SUMMARY

As described above, a company might back up critical computing systemssuch as databases, file servers, web servers, virtual machines, and soon as part of a daily, weekly, or monthly maintenance schedule. Forexample, the company can deploy an information management system thatperiodically performs backup operations or other secondary copyoperations on these critical computing systems.

In some cases, the information management system may be configured tosatisfy certain user-defined or system-defined performance metrics. Forexample, one performance metric can be the number of secondary copyoperation jobs to complete within a threshold period of time. If aninformation management system is unable to satisfy a performance metric,this may result in data loss (e.g., due to data corruption, a failure tobackup primary data, etc.) and/or poor secondary copy operationperformance (e.g., delays in completing secondary copy operation jobs).Thus, it may be important to identify situations in which an informationmanagement system cannot or is unable to satisfy a performance metric sothat the underlying issue can be resolved. If such situations can beidentified and resolved prior to the performance metric not beingsatisfied, this may reduce the likelihood of data loss and/or poorsecondary copy operation performance.

However, identifying situations in which a performance metric cannot oris unable to be satisfied can be difficult. The information managementsystem typically includes multiple components in communication with eachother that collectively manage the performance of secondary copyoperation jobs. Hardware or software issues with any of thesecomponents—such as a hardware failure, high central processing unit(CPU) usage, high disk usage, poor system throughput (e.g., poor datathroughput), a number of secondary copy operation jobs being handled bya component is too high, an operation window may prevent one or moresecondary copy operation jobs from completing, etc.—can cause theinformation management system not to satisfy a performance metric. Itcan be difficult to predict specifically which type of issue may cause afailure to satisfy a performance metric, however, because eachinformation management system may be deployed and configured differentlyto meet a specific entity's needs. For example, a specific type ofhardware failure may cause one information management system to fail tosatisfy a performance metric, but that same type of hardware failure maynot affect another information management system's ability to satisfythe performance metric.

Accordingly, described herein is an information management system thatcan use artificial intelligence to identify situations in which aperformance metric may not be satisfied. For example, the informationmanagement system may include one or more client computing devices thatstore primary data, one or more secondary storage devices that storesecondary copy data, one or more secondary storage computing devicesthat perform secondary copy operation jobs on the primary data and/or onthe secondary copy data, and a storage manager that manages and storesdata related to the execution of secondary copy operation jobs. Thestorage manager can further use artificial intelligence to detectsituations in which a performance metric may not be satisfied (alsoreferred to herein as a “performance metric miss”). In particular, thestorage manager can maintain data related to historical, current, and/orfuture execution of secondary copy operation jobs by the secondarystorage computing device(s), including a time it took to complete asecondary copy operation job, a number of secondary copy operationstreams running simultaneously on a secondary storage computing device,data throughput of a secondary storage computing device while performinga secondary copy operation job, secondary storage computing device CPUusage, secondary storage computing device memory usage, a secondary copyoperation job success rate, a number of secondary copy operation jobsthat failed during a given time period, a length of time to complete aprevious secondary copy operation job, and/or other data described ingreater detail below. Using some or all of this data, the storagemanager can train an artificial intelligence model (e.g., a neuralnetwork) to classify whether a current or future secondary copyoperation job is likely to succeed or fail. Similarly, the storagemanager can use some or all of this data to train another artificialintelligence model (e.g., a machine learning model) to predict thelength of time for a current or future secondary copy operation job tocomplete.

Once trained, the storage manager can use the trained artificialintelligence models to predict whether a performance metric miss mayoccur during a current or future time period. For example, the storagemanager can provide as an input to the neural network data related to acurrent or future execution of one or more secondary copy operationjobs, which causes the neural network to output an indication of whetherany of the secondary copy operation jobs is likely to fail. Before,during, and/or after applying data as an input to the neural network,the storage manager can provide as an input to the machine learningmodel data related to a current or future execution of one or moresecondary copy operation jobs, which causes the machine learning modelto output a prediction of a length of time to complete the secondarycopy operation job(s). If the neural network indicates that all of thecurrent and/or future secondary copy operation jobs may fail, then thestorage manager may determine that a performance metric miss is likelyto occur. However, if the neural network indicates that at least some ofthe current and/or future secondary copy operation jobs are likely tosucceed and the number of jobs that are likely to succeed meets at leasta minimum requirement defined by the performance metric, then thestorage manager can evaluate whether these secondary copy operationjob(s) are likely to succeed within a time defined by the performancemetric using the output of the machine learning model. In particular,the storage manager can identify the length of time to complete thesesecondary copy operation job(s) based on the output of the machinelearning model. The storage manager can then determine whether anyoperation windows (e.g., blackout time windows in which no jobs are tobe performed, maintenance time windows in which no jobs are performeddue to maintenance, etc.) may extend the time for a secondary copyoperation job to complete. If the predicted time plus any additionaltime caused by an operation window exceeds a time defined by theperformance metric, then the storage manager may determine that aperformance metric miss is likely to occur.

If a performance metric miss is detected, the storage manager may causean administrator to be notified of the performance metric miss. Thenotification may also include an indication of the issue(s) identifiedas being the cause of the performance metric miss. In furtherembodiments, the storage manager may cause the identified issue(s) to beresolved such that a performance metric miss does not occur.

One aspect of the disclosure provides a networked information managementsystem comprising a secondary storage computing device. The networkedinformation management system further comprises a second computingdevice in communication with the secondary storage computing device,where the second computing device is configured with computer-executableinstructions that, when executed, cause the second computing device to:obtain data associated with a first secondary copy operation job to beperformed by the secondary storage computing device; apply the obtaineddata as an input to a trained machine learning model, where applicationof the obtained data as an input to the trained machine learning modelcauses the trained machine learning model to output a prediction of alength of time to complete execution of the first secondary copyoperation job; determine that an operation window is scheduled duringthe length of time to complete execution of the first secondary copyoperation job; determine that a performance metric associated with thenetworked information management system will not be satisfied inresponse to the determination that the operation window is scheduledduring the length of the time to complete execution of the firstsecondary copy operation job; and generate a notification in response tothe determination that the performance metric will not be satisfied.

The networked information management system of the preceding paragraphcan include any sub-combination of the following features: wherecomputer-executable instructions, when executed, cause the secondcomputing device to train the machine learning model using machinelearning training data associated with the networked informationmanagement system; where the machine learning training data comprises atleast one of a time taken to perform a second secondary copy operation,a number of secondary copy operation jobs performed by the secondarystorage computing device that have failed within a threshold timeperiod, a number of consecutive secondary copy operation jobs performedby the secondary storage computing device that have failed; or a size ofdata upon which the second secondary copy operation job is performed;where computer-executable instructions, when executed, cause the secondcomputing device to apply at least some of the obtained data as an inputto a trained neural network, where application of the obtained data asan input to the trained neural network causes the trained neural networkto output an indication of whether execution of a second secondary copyoperation job is likely to succeed or fail; where computer-executableinstructions, when executed, cause the second computing device todetermine that the performance metric will not be satisfied in responseto the determination that the operation window is scheduled during thelength of the time to complete execution of the first secondary copyoperation job and in response to a determination that execution of thesecond secondary copy operation job is likely to fail; wherecomputer-executable instructions, when executed, cause the secondcomputing device to determine that the performance metric will besatisfied in response to the determination that the operation window isscheduled during the length of the time to complete execution of thefirst secondary copy operation job and in response to a determinationthat execution of the second secondary copy operation job is likely tosucceed; where the performance metric defines a number of secondary copyoperation jobs to complete within a threshold time period; wherecomputer-executable instructions, when executed, cause the secondcomputing device to determine that the operation window being scheduledduring the length of time to complete execution of the first secondarycopy operation job will delay the first secondary copy operation jobfrom being completed until after the threshold time period expires; andwhere computer-executable instructions, when executed, cause the secondcomputing device to cause the generated notification to appear in a userinterface displayed on a client computing device, where the notificationindicates an explanation of why the performance metric is determined tonot be satisfied.

Another aspect of the disclosure provides a computer-implemented methodcomprising: obtaining data associated with a first secondary copyoperation job to be performed by a secondary storage computing device ina networked information management system; applying the obtained data asan input to a trained machine learning model, where application of theobtained data as an input to the trained machine learning model causesthe trained machine learning model to output a prediction of a length oftime to complete execution of the first secondary copy operation job;determining that an operation window is scheduled during the length oftime to complete execution of the first secondary copy operation job;determining that a performance metric associated with the networkedinformation management system will not be satisfied in response to thedetermination that the operation window is scheduled during the lengthof the time to complete execution of the first secondary copy operationjob; and generating a notification in response to the determination thatthe performance metric will not be satisfied.

The computer-implemented method of the preceding paragraph can includeany sub-combination of the following features: where thecomputer-implemented method further comprises training the machinelearning model using machine learning training data associated with thenetworked information management system; where the machine learningtraining data comprises at least one of a time taken to perform a secondsecondary copy operation, a number of secondary copy operation jobsperformed by the secondary storage computing device that have failedwithin a threshold time period, a number of consecutive secondary copyoperation jobs performed by the secondary storage computing device thathave failed; or a size of data upon which the second secondary copyoperation job is performed; where the computer-implemented methodfurther comprises applying at least some of the obtained data as aninput to a trained neural network, where application of the obtaineddata as an input to the trained neural network causes the trained neuralnetwork to output an indication of whether execution of a secondsecondary copy operation job is likely to succeed or fail; wheredetermining that a performance metric will not be satisfied furthercomprises determining that the performance metric will not be satisfiedin response to the determination that the operation window is scheduledduring the length of the time to complete execution of the firstsecondary copy operation job and in response to a determination thatexecution of the second secondary copy operation job is likely to fail;where the computer-implemented method further comprises determining thatthe performance metric will be satisfied in response to thedetermination that the operation window is scheduled during the lengthof the time to complete execution of the first secondary copy operationjob and in response to a determination that execution of the secondsecondary copy operation job is likely to succeed; where the performancemetric defines a number of secondary copy operation jobs to completewithin a threshold time period; where determining that an operationwindow is scheduled during the length of time to complete execution ofthe first secondary copy operation job further comprises determiningthat the operation window being scheduled during the length of time tocomplete execution of the first secondary copy operation job will delaythe first secondary copy operation job from being completed until afterthe threshold time period expires; and where the computer-implementedmethod further comprises causing the generated notification to appear ina user interface displayed on a client computing device, where thenotification indicates an explanation of why the performance metric isdetermined to not be satisfied.

Another aspect of the disclosure provides a non-transitory computerreadable medium storing computer-executable instructions that, whenexecuted by a networked information management system, cause thenetworked information management system to: obtain data associated witha first secondary copy operation job to be performed by a secondarystorage computing device in the networked information management system;apply the obtained data as an input to a trained machine learning model,where application of the obtained data as an input to the trainedmachine learning model causes the trained machine learning model tooutput a prediction of a length of time to complete execution of thefirst secondary copy operation job; determine that an operation windowis scheduled during the length of time to complete execution of thefirst secondary copy operation job; determine that a performance metricassociated with the networked information management system will not besatisfied in response to the determination that the operation window isscheduled during the length of the time to complete execution of thefirst secondary copy operation job; and generate a notification inresponse to the determination that the performance metric will not besatisfied.

The non-transitory computer readable medium of the preceding paragraphcan include any sub-combination of the following features: where thecomputer-executable instructions, when executed, further cause thenetworked information management system to: apply at least some of theobtained data as an input to a trained neural network, where applicationof the obtained data as an input to the trained neural network causesthe trained neural network to output an indication of whether executionof a second secondary copy operation job is likely to succeed or fail,and determine that the performance metric will not be satisfied inresponse to the determination that the operation window is scheduledduring the length of the time to complete execution of the firstsecondary copy operation job and in response to a determination thatexecution of the second secondary copy operation job is likely to fail.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a block diagram illustrating an exemplary informationmanagement system.

FIG. 1B is a detailed view of a primary storage device, a secondarystorage device, and some examples of primary data and secondary copydata.

FIG. 1C is a block diagram of an exemplary information management systemincluding a storage manager, one or more data agents, and one or moremedia agents.

FIG. 1D is a block diagram illustrating a scalable informationmanagement system.

FIG. 1E illustrates certain secondary copy operations according to anexemplary storage policy.

FIGS. 1F-1H are block diagrams illustrating suitable data structuresthat may be employed by the information management system.

FIG. 2A illustrates a system and technique for synchronizing primarydata to a destination such as a failover site using secondary copy data.

FIG. 2B illustrates an information management system architectureincorporating use of a network file system (NFS) protocol forcommunicating between the primary and secondary storage subsystems.

FIG. 2C is a block diagram of an example of a highly scalable manageddata pool architecture.

FIG. 3 is a block diagram illustrating some salient portions of a systemfor predicting performance metric misses, according to an embodiment.

FIG. 4 is a block diagram illustrating some salient portions of anenvironment for auto-tuning information management systems, according toan embodiment.

FIG. 5A illustrates a block diagram showing the operations performed todetect an anomaly.

FIG. 5B illustrates a block diagram showing the operations performed topredict a length of time it may take for a first secondary copyoperation job to complete.

FIG. 5C illustrates a block diagram showing the operations performed topredict whether execution of a first secondary copy operation job willsucceed or fail.

FIG. 5D illustrates a block diagram showing the operations performed todetermine whether a performance metric miss will occur.

FIG. 6 illustrates a block diagram showing the operations performed toauto-tune an information management system.

FIG. 7 depicts some salient operations of a method for predicting aperformance metric miss, according to an embodiment.

FIG. 8 depicts some salient operations of another method for predictinga performance metric miss, according to an embodiment.

FIG. 9 depicts some salient operations of a method for auto-tuning orautomatically configuring an information management system, according toan embodiment.

FIG. 10 depicts a graphical user interface showing a performance metricmiss notification or alert, according to an embodiment.

DETAILED DESCRIPTION

As described above, a company might back up critical computing systemssuch as databases, file servers, web servers, virtual machines, and soon as part of a daily, weekly, or monthly maintenance schedule. Forexample, the company can deploy an information management system thatperiodically performs backup operations or other secondary copyoperations on these critical computing systems.

In some cases, the information management system may be configured tosatisfy certain user-defined or system-defined performance metrics. Forexample, one performance metric can be the number of secondary copyoperation jobs to complete within a threshold period of time. If aninformation management system is unable to satisfy a performance metric,this may result in data loss (e.g., due to data corruption, a failure tobackup primary data, etc.) and/or poor secondary copy operationperformance (e.g., delays in completing secondary copy operation jobs).Thus, it may be important to identify situations in which an informationmanagement system cannot or is unable to satisfy a performance metric sothat the underlying issue can be resolved. If such situations can beidentified and resolved prior to the performance metric not beingsatisfied, this may reduce the likelihood of data loss and/or poorsecondary copy operation performance.

However, identifying situations in which a performance metric cannot oris unable to be satisfied can be difficult. The information managementsystem typically includes multiple components in communication with eachother that collectively manage the performance of secondary copyoperation jobs. Hardware or software issues with any of thesecomponents—such as a hardware failure, high central processing unit(CPU) usage, high disk usage, poor system throughput (e.g., poor datathroughput), a number of secondary copy operation jobs being handled bya component is too high, an operation window may prevent one or moresecondary copy operation jobs from completing, etc.—can cause theinformation management system not to satisfy a performance metric. Itcan be difficult to predict specifically which type of issue may cause afailure to satisfy a performance metric, however, because eachinformation management system may be deployed and configured differentlyto meet a specific entity's needs. For example, a specific type ofhardware failure may cause one information management system to fail tosatisfy a performance metric, but that same type of hardware failure maynot affect another information management system's ability to satisfythe performance metric.

Accordingly, described herein is an information management system thatcan use artificial intelligence to identify situations in which aperformance metric may not be satisfied. For example, the informationmanagement system may include one or more client computing devices thatstore primary data, one or more secondary storage devices that storesecondary copy data, one or more secondary storage computing devicesthat perform secondary copy operation jobs on the primary data and/or onthe secondary copy data, and a storage manager that manages and storesdata related to the execution of secondary copy operation jobs. Thestorage manager can further use artificial intelligence to detectsituations in which a performance metric may not be satisfied (alsoreferred to herein as a “performance metric miss”). In particular, thestorage manager can maintain data related to historical, current, and/orfuture execution of secondary copy operation jobs by the secondarystorage computing device(s), including a time it took to complete asecondary copy operation job, a number of secondary copy operationstreams running simultaneously on a secondary storage computing device,data throughput of a secondary storage computing device while performinga secondary copy operation job, secondary storage computing device CPUusage, secondary storage computing device memory usage, a secondary copyoperation job success rate, a number of secondary copy operation jobsthat failed during a given time period, a length of time to complete aprevious secondary copy operation job, and/or other data described ingreater detail below. Using some or all of this data, the storagemanager can train an artificial intelligence model (e.g., a neuralnetwork) to classify whether a current or future secondary copyoperation job is likely to succeed or fail. Similarly, the storagemanager can use some or all of this data to train another artificialintelligence model (e.g., a machine learning model) to predict thelength of time for a current or future secondary copy operation job tocomplete.

Once trained, the storage manager can use the trained artificialintelligence models to predict whether a performance metric miss mayoccur during a current or future time period. For example, the storagemanager can provide as an input to the neural network data related to acurrent or future execution of one or more secondary copy operationjobs, which causes the neural network to output an indication of whetherany of the secondary copy operation jobs is likely to fail. Before,during, and/or after applying data as an input to the neural network,the storage manager can provide as an input to the machine learningmodel data related to a current or future execution of one or moresecondary copy operation jobs, which causes the machine learning modelto output a prediction of a length of time to complete the secondarycopy operation job(s). If the neural network indicates that all of thecurrent and/or future secondary copy operation jobs may fail, then thestorage manager may determine that a performance metric miss is likelyto occur. However, if the neural network indicates that at least some ofthe current and/or future secondary copy operation jobs are likely tosucceed and the number of jobs that are likely to succeed meets at leasta minimum requirement defined by the performance metric, then thestorage manager can evaluate whether these secondary copy operationjob(s) are likely to succeed within a time defined by the performancemetric using the output of the machine learning model. In particular,the storage manager can identify the length of time to complete thesesecondary copy operation job(s) based on the output of the machinelearning model. The storage manager can then determine whether anyoperation windows (e.g., blackout time windows in which no jobs are tobe performed, maintenance time windows in which no jobs are performeddue to maintenance, etc.) may extend the time for a secondary copyoperation job to complete. If the predicted time plus any additionaltime caused by an operation window exceeds a time defined by theperformance metric, then the storage manager may determine that aperformance metric miss is likely to occur.

If a performance metric miss is detected, the storage manager may causean administrator to be notified of the performance metric miss. Thenotification may also include an indication of the issue(s) identifiedas being the cause of the performance metric miss. In furtherembodiments, the storage manager may cause the identified issue(s) to beresolved such that a performance metric miss does not occur.

Detailed descriptions and examples of systems and methods according toone or more embodiments may be found in the section entitled PerformanceMetric Miss Prediction, as well as in the section entitled ExampleEmbodiments, and also in FIGS. 3 through 10 herein. Furthermore,components and functionality for detecting performance metric misses maybe configured and/or incorporated into information management systemssuch as those described herein in FIGS. 1A-1H and 2A-2C.

Various embodiments described herein are intimately tied to, enabled by,and would not exist except for, computer technology. For example, usingartificial intelligence to detect performance metric misses describedherein in reference to various embodiments cannot reasonably beperformed by humans alone, without the computer technology upon whichthey are implemented.

Information Management System Overview

With the increasing importance of protecting and leveraging data,organizations simply cannot risk losing critical data. Moreover, runawaydata growth and other modern realities make protecting and managing dataincreasingly difficult. There is therefore a need for efficient,powerful, and user-friendly solutions for protecting and managing dataand for smart and efficient management of data storage. Depending on thesize of the organization, there may be many data production sourceswhich are under the purview of tens, hundreds, or even thousands ofindividuals. In the past, individuals were sometimes responsible formanaging and protecting their own data, and a patchwork of hardware andsoftware point solutions may have been used in any given organization.These solutions were often provided by different vendors and had limitedor no interoperability. Certain embodiments described herein addressthese and other shortcomings of prior approaches by implementingscalable, unified, organization-wide information management, includingdata storage management.

FIG. 1A shows one such information management system 100 (or “system100”), which generally includes combinations of hardware and softwareconfigured to protect and manage data and metadata that are generatedand used by computing devices in system 100. System 100 may be referredto in some embodiments as a “storage management system” or a “datastorage management system.” System 100 performs information managementoperations, some of which may be referred to as “storage operations” or“data storage operations,” to protect and manage the data residing inand/or managed by system 100. The organization that employs system 100may be a corporation or other business entity, non-profit organization,educational institution, household, governmental agency, or the like.

Generally, the systems and associated components described herein may becompatible with and/or provide some or all of the functionality of thesystems and corresponding components described in one or more of thefollowing U.S. patents/publications and patent applications assigned toCommvault Systems, Inc., each of which is hereby incorporated byreference in its entirety herein:

-   -   U.S. Pat. No. 7,035,880, entitled “Modular Backup and Retrieval        System Used in Conjunction With a Storage Area Network”;    -   U.S. Pat. No. 7,107,298, entitled “System And Method For        Archiving Objects In An Information Store”;    -   U.S. Pat. No. 7,246,207, entitled “System and Method for        Dynamically Performing Storage Operations in a Computer        Network”;    -   U.S. Pat. No. 7,315,923, entitled “System And Method For        Combining Data Streams In Pipelined Storage Operations In A        Storage Network”;    -   U.S. Pat. No. 7,343,453, entitled “Hierarchical Systems and        Methods for Providing a Unified View of Storage Information”;    -   U.S. Pat. No. 7,395,282, entitled “Hierarchical Backup and        Retrieval System”;    -   U.S. Pat. No. 7,529,782, entitled “System and Methods for        Performing a Snapshot and for Restoring Data”;    -   U.S. Pat. No. 7,617,262, entitled “System and Methods for        Monitoring Application Data in a Data Replication System”;    -   U.S. Pat. No. 7,734,669, entitled “Managing Copies Of Data”;    -   U.S. Pat. No. 7,747,579, entitled “Metabase for Facilitating        Data Classification”;    -   U.S. Pat. No. 8,156,086, entitled “Systems And Methods For        Stored Data Verification”;    -   U.S. Pat. No. 8,170,995, entitled “Method and System for Offline        Indexing of Content and Classifying Stored Data”;    -   U.S. Pat. No. 8,230,195, entitled “System And Method For        Performing Auxiliary Storage Operations”;    -   U.S. Pat. No. 8,285,681, entitled “Data Object Store and Server        for a Cloud Storage Environment, Including Data Deduplication        and Data Management Across Multiple Cloud Storage Sites”;    -   U.S. Pat. No. 8,307,177, entitled “Systems And Methods For        Management Of Virtualization Data”;    -   U.S. Pat. No. 8,364,652, entitled “Content-Aligned, Block-Based        Deduplication”;    -   U.S. Pat. No. 8,578,120, entitled “Block-Level Single        Instancing”;    -   U.S. Pat. No. 8,954,446, entitled “Client-Side Repository in a        Networked Deduplicated Storage System”;    -   U.S. Pat. No. 9,020,900, entitled “Distributed Deduplicated        Storage System”;    -   U.S. Pat. No. 9,098,495, entitled “Application-Aware and Remote        Single Instance Data Management”;    -   U.S. Pat. No. 9,239,687, entitled “Systems and Methods for        Retaining and Using Data Block Signatures in Data Protection        Operations”;    -   U.S. Patent Application Pub. No. 2006/0224846, entitled “System        and Method to Support Single Instance Storage Operations”;    -   U.S. Patent Application Pub. No. 2014/0201170, entitled “High        Availability Distributed Deduplicated Storage System”;    -   U.S. Patent Application Pub. No. 2016/0350391, entitled        “Replication Using Deduplicated Secondary Copy Data”;    -   U.S. Patent Application Pub. No. 2017/0168903 entitled “Live        Synchronization and Management of Virtual Machines across        Computing and Virtualization Platforms and Using Live        Synchronization to Support Disaster Recovery”;    -   U.S. Patent Application Pub. No. 2017/0193003 entitled        “Redundant and Robust Distributed Deduplication Data Storage        System”;    -   U.S. Patent Application Pub. No. 2017/0235647 entitled “Data        Protection Operations Based on Network Path Information”;    -   U.S. Patent Application Pub. No. 2017/0242871, entitled “Data        Restoration Operations Based on Network Path Information”; and    -   U.S. Patent Application Pub. No. 2017/0185488, entitled        “Application-Level Live Synchronization Across Computing        Platforms Including Synchronizing Co-Resident Applications To        Disparate Standby Destinations And Selectively Synchronizing        Some Applications And Not Others”.

System 100 includes computing devices and computing technologies. Forinstance, system 100 can include one or more client computing devices102 and secondary storage computing devices 106, as well as storagemanager 140 or a host computing device for it. Computing devices caninclude, without limitation, one or more: workstations, personalcomputers, desktop computers, or other types of generally fixedcomputing systems such as mainframe computers, servers, andminicomputers. Other computing devices can include mobile or portablecomputing devices, such as one or more laptops, tablet computers,personal data assistants, mobile phones (such as smartphones), and othermobile or portable computing devices such as embedded computers, set topboxes, vehicle-mounted devices, wearable computers, etc. Servers caninclude mail servers, file servers, database servers, virtual machineservers, and web servers. Any given computing device comprises one ormore processors (e.g., CPU and/or single-core or multi-core processors),as well as corresponding non-transitory computer memory (e.g.,random-access memory (RAM)) for storing computer programs which are tobe executed by the one or more processors. Other computer memory formass storage of data may be packaged/configured with the computingdevice (e.g., an internal hard disk) and/or may be external andaccessible by the computing device (e.g., network-attached storage, astorage array, etc.). In some cases, a computing device includes cloudcomputing resources, which may be implemented as virtual machines. Forinstance, one or more virtual machines may be provided to theorganization by a third-party cloud service vendor.

In some embodiments, computing devices can include one or more virtualmachine(s) running on a physical host computing device (or “hostmachine”) operated by the organization. As one example, the organizationmay use one virtual machine as a database server and another virtualmachine as a mail server, both virtual machines operating on the samehost machine. A Virtual machine (“VM”) is a software implementation of acomputer that does not physically exist and is instead instantiated inan operating system of a physical computer (or host machine) to enableapplications to execute within the VM's environment, i.e., a VM emulatesa physical computer. A VM includes an operating system and associatedvirtual resources, such as computer memory and processor(s). Ahypervisor operates between the VM and the hardware of the physical hostmachine and is generally responsible for creating and running the VMs.Hypervisors are also known in the art as virtual machine monitors or avirtual machine managers or “VMMs”, and may be implemented in software,firmware, and/or specialized hardware installed on the host machine.Examples of hypervisors include ESX Server, by VMware, Inc. of PaloAlto, Calif.; Microsoft Virtual Server and Microsoft Windows ServerHyper-V, both by Microsoft Corporation of Redmond, Wash.; Sun xVM byOracle America Inc. of Santa Clara, Calif.; and Xen by Citrix Systems,Santa Clara, Calif. The hypervisor provides resources to each virtualoperating system such as a virtual processor, virtual memory, a virtualnetwork device, and a virtual disk. Each virtual machine has one or moreassociated virtual disks. The hypervisor typically stores the data ofvirtual disks in files on the file system of the physical host machine,called virtual machine disk files (“VMDK” in VMware lingo) or virtualhard disk image files (in Microsoft lingo). For example, VMware's ESXServer provides the Virtual Machine File System (VMFS) for the storageof virtual machine disk files. A virtual machine reads data from andwrites data to its virtual disk much the way that a physical machinereads data from and writes data to a physical disk. Examples oftechniques for implementing information management in a cloud computingenvironment are described in U.S. Pat. No. 8,285,681. Examples oftechniques for implementing information management in a virtualizedcomputing environment are described in U.S. Pat. No. 8,307,177.

Information management system 100 can also include electronic datastorage devices, generally used for mass storage of data, including,e.g., primary storage devices 104 and secondary storage devices 108.Storage devices can generally be of any suitable type including, withoutlimitation, disk drives, storage arrays (e.g., storage-area network(SAN) and/or network-attached storage (NAS) technology), semiconductormemory (e.g., solid state storage devices), network attached storage(NAS) devices, tape libraries, or other magnetic, non-tape storagedevices, optical media storage devices, DNA/RNA-based memory technology,combinations of the same, etc. In some embodiments, storage devices formpart of a distributed file system. In some cases, storage devices areprovided in a cloud storage environment (e.g., a private cloud or oneoperated by a third-party vendor), whether for primary data or secondarycopies or both.

Depending on context, the term “information management system” can referto generally all of the illustrated hardware and software components inFIG. 1C, or the term may refer to only a subset of the illustratedcomponents. For instance, in some cases, system 100 generally refers toa combination of specialized components used to protect, move, manage,manipulate, analyze, and/or process data and metadata generated byclient computing devices 102. However, system 100 in some cases does notinclude the underlying components that generate and/or store primarydata 112, such as the client computing devices 102 themselves, and theprimary storage devices 104. Likewise secondary storage devices 108(e.g., a third-party provided cloud storage environment) may not be partof system 100. As an example, “information management system” or“storage management system” may sometimes refer to one or more of thefollowing components, which will be described in further detail below:storage manager, data agent, and media agent.

One or more client computing devices 102 may be part of system 100, eachclient computing device 102 having an operating system and at least oneapplication 110 and one or more accompanying data agents executingthereon; and associated with one or more primary storage devices 104storing primary data 112. Client computing device(s) 102 and primarystorage devices 104 may generally be referred to in some cases asprimary storage subsystem 117.

Client Computing Devices, Clients, and Subclients

Typically, a variety of sources in an organization produce data to beprotected and managed. As just one example, in a corporate environmentsuch data sources can be employee workstations and company servers suchas a mail server, a web server, a database server, a transaction server,or the like. In system 100, data generation sources include one or moreclient computing devices 102. A computing device that has a data agent142 installed and operating on it is generally referred to as a “clientcomputing device” 102, and may include any type of computing device,without limitation. A client computing device 102 may be associated withone or more users and/or user accounts.

A “client” is a logical component of information management system 100,which may represent a logical grouping of one or more data agentsinstalled on a client computing device 102. Storage manager 140recognizes a client as a component of system 100, and in someembodiments, may automatically create a client component the first timea data agent 142 is installed on a client computing device 102. Becausedata generated by executable component(s) 110 is tracked by theassociated data agent 142 so that it may be properly protected in system100, a client may be said to generate data and to store the generateddata to primary storage, such as primary storage device 104. However,the terms “client” and “client computing device” as used herein do notimply that a client computing device 102 is necessarily configured inthe client/server sense relative to another computing device such as amail server, or that a client computing device 102 cannot be a server inits own right. As just a few examples, a client computing device 102 canbe and/or include mail servers, file servers, database servers, virtualmachine servers, and/or web servers.

Each client computing device 102 may have application(s) 110 executingthereon which generate and manipulate the data that is to be protectedfrom loss and managed in system 100. Applications 110 generallyfacilitate the operations of an organization, and can include, withoutlimitation, mail server applications (e.g., Microsoft Exchange Server),file system applications, mail client applications (e.g., MicrosoftExchange Client), database applications or database management systems(e.g., SQL, Oracle, SAP, Lotus Notes Database), word processingapplications (e.g., Microsoft Word), spreadsheet applications, financialapplications, presentation applications, graphics and/or videoapplications, browser applications, mobile applications, entertainmentapplications, and so on. Each application 110 may be accompanied by anapplication-specific data agent 142, though not all data agents 142 areapplication-specific or associated with only application. A file system,e.g., Microsoft Windows Explorer, may be considered an application 110and may be accompanied by its own data agent 142. Client computingdevices 102 can have at least one operating system (e.g., MicrosoftWindows, Mac OS X, iOS, IBM z/OS, Linux, other Unix-based operatingsystems, etc.) installed thereon, which may support or host one or morefile systems and other applications 110. In some embodiments, a virtualmachine that executes on a host client computing device 102 may beconsidered an application 110 and may be accompanied by a specific dataagent 142 (e.g., virtual server data agent).

Client computing devices 102 and other components in system 100 can beconnected to one another via one or more electronic communicationpathways 114. For example, a first communication pathway 114 maycommunicatively couple client computing device 102 and secondary storagecomputing device 106; a second communication pathway 114 maycommunicatively couple storage manager 140 and client computing device102; and a third communication pathway 114 may communicatively couplestorage manager 140 and secondary storage computing device 106, etc.(see, e.g., FIG. 1A and FIG. 1C). A communication pathway 114 caninclude one or more networks or other connection types including one ormore of the following, without limitation: the Internet, a wide areanetwork (WAN), a local area network (LAN), a Storage Area Network (SAN),a Fibre Channel (FC) connection, a Small Computer System Interface(SCSI) connection, a virtual private network (VPN), a token ring orTCP/IP based network, an intranet network, a point-to-point link, acellular network, a wireless data transmission system, a two-way cablesystem, an interactive kiosk network, a satellite network, a broadbandnetwork, a baseband network, a neural network, a mesh network, an ad hocnetwork, other appropriate computer or telecommunications networks,combinations of the same or the like. Communication pathways 114 in somecases may also include application programming interfaces (APIs)including, e.g., cloud service provider APIs, virtual machine managementAPIs, and hosted service provider APIs. The underlying infrastructure ofcommunication pathways 114 may be wired and/or wireless, analog and/ordigital, or any combination thereof; and the facilities used may beprivate, public, third-party provided, or any combination thereof,without limitation.

A “subclient” is a logical grouping of all or part of a client's primarydata 112. In general, a subclient may be defined according to how thesubclient data is to be protected as a unit in system 100. For example,a subclient may be associated with a certain storage policy. A givenclient may thus comprise several subclients, each subclient associatedwith a different storage policy. For example, some files may form afirst subclient that requires compression and deduplication and isassociated with a first storage policy. Other files of the client mayform a second subclient that requires a different retention schedule aswell as encryption, and may be associated with a different, secondstorage policy. As a result, though the primary data may be generated bythe same application 110 and may belong to one given client, portions ofthe data may be assigned to different subclients for distinct treatmentby system 100. More detail on subclients is given in regard to storagepolicies below.

Primary Data and Exemplary Primary Storage Devices

Primary data 112 is generally production data or “live” data generatedby the operating system and/or applications 110 executing on clientcomputing device 102. Primary data 112 is generally stored on primarystorage device(s) 104 and is organized via a file system operating onthe client computing device 102. Thus, client computing device(s) 102and corresponding applications 110 may create, access, modify, write,delete, and otherwise use primary data 112. Primary data 112 isgenerally in the native format of the source application 110. Primarydata 112 is an initial or first stored body of data generated by thesource application 110. Primary data 112 in some cases is createdsubstantially directly from data generated by the corresponding sourceapplication 110. It can be useful in performing certain tasks toorganize primary data 112 into units of different granularities. Ingeneral, primary data 112 can include files, directories, file systemvolumes, data blocks, extents, or any other hierarchies or organizationsof data objects. As used herein, a “data object” can refer to (i) anyfile that is currently addressable by a file system or that waspreviously addressable by the file system (e.g., an archive file),and/or to (ii) a subset of such a file (e.g., a data block, an extent,etc.). Primary data 112 may include structured data (e.g., databasefiles), unstructured data (e.g., documents), and/or semi-structureddata. See, e.g., FIG. 1B.

It can also be useful in performing certain functions of system 100 toaccess and modify metadata within primary data 112. Metadata generallyincludes information about data objects and/or characteristicsassociated with the data objects. For simplicity herein, it is to beunderstood that, unless expressly stated otherwise, any reference toprimary data 112 generally also includes its associated metadata, butreferences to metadata generally do not include the primary data.Metadata can include, without limitation, one or more of the following:the data owner (e.g., the client or user that generates the data), thelast modified time (e.g., the time of the most recent modification ofthe data object), a data object name (e.g., a file name), a data objectsize (e.g., a number of bytes of data), information about the content(e.g., an indication as to the existence of a particular search term),user-supplied tags, to/from information for email (e.g., an emailsender, recipient, etc.), creation date, file type (e.g., format orapplication type), last accessed time, application type (e.g., type ofapplication that generated the data object), location/network (e.g., acurrent, past or future location of the data object and network pathwaysto/from the data object), geographic location (e.g., GPS coordinates),frequency of change (e.g., a period in which the data object ismodified), business unit (e.g., a group or department that generates,manages or is otherwise associated with the data object), aginginformation (e.g., a schedule, such as a time period, in which the dataobject is migrated to secondary or long term storage), boot sectors,partition layouts, file location within a file folder directorystructure, user permissions, owners, groups, access control lists(ACLs), system metadata (e.g., registry information), combinations ofthe same or other similar information related to the data object. Inaddition to metadata generated by or related to file systems andoperating systems, some applications 110 and/or other components ofsystem 100 maintain indices of metadata for data objects, e.g., metadataassociated with individual email messages. The use of metadata toperform classification and other functions is described in greaterdetail below.

Primary storage devices 104 storing primary data 112 may be relativelyfast and/or expensive technology (e.g., flash storage, a disk drive, ahard-disk storage array, solid state memory, etc.), typically to supporthigh-performance live production environments. Primary data 112 may behighly changeable and/or may be intended for relatively short termretention (e.g., hours, days, or weeks). According to some embodiments,client computing device 102 can access primary data 112 stored inprimary storage device 104 by making conventional file system calls viathe operating system. Each client computing device 102 is generallyassociated with and/or in communication with one or more primary storagedevices 104 storing corresponding primary data 112. A client computingdevice 102 is said to be associated with or in communication with aparticular primary storage device 104 if it is capable of one or moreof: routing and/or storing data (e.g., primary data 112) to the primarystorage device 104, coordinating the routing and/or storing of data tothe primary storage device 104, retrieving data from the primary storagedevice 104, coordinating the retrieval of data from the primary storagedevice 104, and modifying and/or deleting data in the primary storagedevice 104. Thus, a client computing device 102 may be said to accessdata stored in an associated storage device 104.

Primary storage device 104 may be dedicated or shared. In some cases,each primary storage device 104 is dedicated to an associated clientcomputing device 102, e.g., a local disk drive. In other cases, one ormore primary storage devices 104 can be shared by multiple clientcomputing devices 102, e.g., via a local network, in a cloud storageimplementation, etc. As one example, primary storage device 104 can be astorage array shared by a group of client computing devices 102, such asEMC Clariion, EMC Symmetrix, EMC Celerra, Dell EqualLogic, IBM XIV,NetApp FAS, HP EVA, and HP 3PAR.

System 100 may also include hosted services (not shown), which may behosted in some cases by an entity other than the organization thatemploys the other components of system 100. For instance, the hostedservices may be provided by online service providers. Such serviceproviders can provide social networking services, hosted email services,or hosted productivity applications or other hosted applications such assoftware-as-a-service (SaaS), platform-as-a-service (PaaS), applicationservice providers (ASPs), cloud services, or other mechanisms fordelivering functionality via a network. As it services users, eachhosted service may generate additional data and metadata, which may bemanaged by system 100, e.g., as primary data 112. In some cases, thehosted services may be accessed using one of the applications 110. As anexample, a hosted mail service may be accessed via browser running on aclient computing device 102.

Secondary Copies and Exemplary Secondary Storage Devices

Primary data 112 stored on primary storage devices 104 may becompromised in some cases, such as when an employee deliberately oraccidentally deletes or overwrites primary data 112. Or primary storagedevices 104 can be damaged, lost, or otherwise corrupted. For recoveryand/or regulatory compliance purposes, it is therefore useful togenerate and maintain copies of primary data 112. Accordingly, system100 includes one or more secondary storage computing devices 106 and oneor more secondary storage devices 108 configured to create and store oneor more secondary copies 116 of primary data 112 including itsassociated metadata. The secondary storage computing devices 106 and thesecondary storage devices 108 may be referred to as secondary storagesubsystem 118.

Secondary copies 116 can help in search and analysis efforts and meetother information management goals as well, such as: restoring dataand/or metadata if an original version is lost (e.g., by deletion,corruption, or disaster); allowing point-in-time recovery; complyingwith regulatory data retention and electronic discovery (e-discovery)requirements; reducing utilized storage capacity in the productionsystem and/or in secondary storage; facilitating organization and searchof data; improving user access to data files across multiple computingdevices and/or hosted services; and implementing data retention andpruning policies.

A secondary copy 116 can comprise a separate stored copy of data that isderived from one or more earlier-created stored copies (e.g., derivedfrom primary data 112 or from another secondary copy 116). Secondarycopies 116 can include point-in-time data, and may be intended forrelatively long-term retention before some or all of the data is movedto other storage or discarded. In some cases, a secondary copy 116 maybe in a different storage device than other previously stored copies;and/or may be remote from other previously stored copies. Secondarycopies 116 can be stored in the same storage device as primary data 112.For example, a disk array capable of performing hardware snapshotsstores primary data 112 and creates and stores hardware snapshots of theprimary data 112 as secondary copies 116. Secondary copies 116 may bestored in relatively slow and/or lower cost storage (e.g., magnetictape). A secondary copy 116 may be stored in a backup or archive format,or in some other format different from the native source applicationformat or other format of primary data 112.

Secondary storage computing devices 106 may index secondary copies 116(e.g., using a media agent 144), enabling users to browse and restore ata later time and further enabling the lifecycle management of theindexed data. After creation of a secondary copy 116 that representscertain primary data 112, a pointer or other location indicia (e.g., astub) may be placed in primary data 112, or be otherwise associated withprimary data 112, to indicate the current location of a particularsecondary copy 116. Since an instance of a data object or metadata inprimary data 112 may change over time as it is modified by application110 (or hosted service or the operating system), system 100 may createand manage multiple secondary copies 116 of a particular data object ormetadata, each copy representing the state of the data object in primarydata 112 at a particular point in time. Moreover, since an instance of adata object in primary data 112 may eventually be deleted from primarystorage device 104 and the file system, system 100 may continue tomanage point-in-time representations of that data object, even thoughthe instance in primary data 112 no longer exists. For virtual machines,the operating system and other applications 110 of client computingdevice(s) 102 may execute within or under the management ofvirtualization software (e.g., a VMM), and the primary storage device(s)104 may comprise a virtual disk created on a physical storage device.System 100 may create secondary copies 116 of the files or other dataobjects in a virtual disk file and/or secondary copies 116 of the entirevirtual disk file itself (e.g., of an entire .vmdk file).

Secondary copies 116 are distinguishable from corresponding primary data112. First, secondary copies 116 can be stored in a different formatfrom primary data 112 (e.g., backup, archive, or other non-nativeformat). For this or other reasons, secondary copies 116 may not bedirectly usable by applications 110 or client computing device 102(e.g., via standard system calls or otherwise) without modification,processing, or other intervention by system 100 which may be referred toas “restore” operations. Secondary copies 116 may have been processed bydata agent 142 and/or media agent 144 in the course of being created(e.g., compression, deduplication, encryption, integrity markers,indexing, formatting, application-aware metadata, etc.), and thussecondary copy 116 may represent source primary data 112 withoutnecessarily being exactly identical to the source.

Second, secondary copies 116 may be stored on a secondary storage device108 that is inaccessible to application 110 running on client computingdevice 102 and/or hosted service. Some secondary copies 116 may be“offline copies,” in that they are not readily available (e.g., notmounted to tape or disk). Offline copies can include copies of data thatsystem 100 can access without human intervention (e.g., tapes within anautomated tape library, but not yet mounted in a drive), and copies thatthe system 100 can access only with some human intervention (e.g., tapeslocated at an offsite storage site).

Using Intermediate Devices for Creating Secondary Copies—SecondaryStorage Computing Devices

Creating secondary copies can be challenging when hundreds or thousandsof client computing devices 102 continually generate large volumes ofprimary data 112 to be protected. Also, there can be significantoverhead involved in the creation of secondary copies 116. Moreover,specialized programmed intelligence and/or hardware capability isgenerally needed for accessing and interacting with secondary storagedevices 108. Client computing devices 102 may interact directly with asecondary storage device 108 to create secondary copies 116, but in viewof the factors described above, this approach can negatively impact theability of client computing device 102 to serve/service application 110and produce primary data 112. Further, any given client computing device102 may not be optimized for interaction with certain secondary storagedevices 108.

Thus, system 100 may include one or more software and/or hardwarecomponents which generally act as intermediaries between clientcomputing devices 102 (that generate primary data 112) and secondarystorage devices 108 (that store secondary copies 116). In addition tooff-loading certain responsibilities from client computing devices 102,these intermediate components provide other benefits. For instance, asdiscussed further below with respect to FIG. 1D, distributing some ofthe work involved in creating secondary copies 116 can enhancescalability and improve system performance. For instance, usingspecialized secondary storage computing devices 106 and media agents 144for interfacing with secondary storage devices 108 and/or for performingcertain data processing operations can greatly improve the speed withwhich system 100 performs information management operations and can alsoimprove the capacity of the system to handle large numbers of suchoperations, while reducing the computational load on the productionenvironment of client computing devices 102. The intermediate componentscan include one or more secondary storage computing devices 106 as shownin FIG. 1A and/or one or more media agents 144. Media agents arediscussed further below (e.g., with respect to FIGS. 1C-1E). Thesespecial-purpose components of system 100 comprise specialized programmedintelligence and/or hardware capability for writing to, reading from,instructing, communicating with, or otherwise interacting with secondarystorage devices 108.

Secondary storage computing device(s) 106 can comprise any of thecomputing devices described above, without limitation. In some cases,secondary storage computing device(s) 106 also include specializedhardware componentry and/or software intelligence (e.g., specializedinterfaces) for interacting with certain secondary storage device(s) 108with which they may be specially associated.

To create a secondary copy 116 involving the copying of data fromprimary storage subsystem 117 to secondary storage subsystem 118, clientcomputing device 102 may communicate the primary data 112 to be copied(or a processed version thereof generated by a data agent 142) to thedesignated secondary storage computing device 106, via a communicationpathway 114. Secondary storage computing device 106 in turn may furtherprocess and convey the data or a processed version thereof to secondarystorage device 108. One or more secondary copies 116 may be created fromexisting secondary copies 116, such as in the case of an auxiliary copyoperation, described further below.

Exemplary Primary Data and an Exemplary Secondary Copy

FIG. 1B is a detailed view of some specific examples of primary datastored on primary storage device(s) 104 and secondary copy data storedon secondary storage device(s) 108, with other components of the systemremoved for the purposes of illustration. Stored on primary storagedevice(s) 104 are primary data 112 objects including word processingdocuments 119A-B, spreadsheets 120, presentation documents 122, videofiles 124, image files 126, email mailboxes 128 (and corresponding emailmessages 129A-C), HTML/XML or other types of markup language files 130,databases 132 and corresponding tables or other data structures133A-133C. Some or all primary data 112 objects are associated withcorresponding metadata (e.g., “Meta1-11”), which may include file systemmetadata and/or application-specific metadata. Stored on the secondarystorage device(s) 108 are secondary copy 116 data objects 134A-C whichmay include copies of or may otherwise represent corresponding primarydata 112.

Secondary copy data objects 134A-C can individually represent more thanone primary data object. For example, secondary copy data object 134Arepresents three separate primary data objects 133C, 122, and 129C(represented as 133C′, 122′, and 129C′, respectively, and accompanied bycorresponding metadata Meta11, Meta3, and Meta8, respectively).Moreover, as indicated by the prime mark (′), secondary storagecomputing devices 106 or other components in secondary storage subsystem118 may process the data received from primary storage subsystem 117 andstore a secondary copy including a transformed and/or supplementedrepresentation of a primary data object and/or metadata that isdifferent from the original format, e.g., in a compressed, encrypted,deduplicated, or other modified format. For instance, secondary storagecomputing devices 106 can generate new metadata or other informationbased on said processing, and store the newly generated informationalong with the secondary copies. Secondary copy data object 1346represents primary data objects 120, 1336, and 119A as 120′, 1336′, and119A′, respectively, accompanied by corresponding metadata Meta2,Meta10, and Meta1, respectively. Also, secondary copy data object 134Crepresents primary data objects 133A, 1196, and 129A as 133A′, 1196′,and 129A′, respectively, accompanied by corresponding metadata Meta9,Meta5, and Meta6, respectively.

Exemplary Information Management System Architecture

System 100 can incorporate a variety of different hardware and softwarecomponents, which can in turn be organized with respect to one anotherin many different configurations, depending on the embodiment. There arecritical design choices involved in specifying the functionalresponsibilities of the components and the role of each component insystem 100. Such design choices can impact how system 100 performs andadapts to data growth and other changing circumstances. FIG. 1C shows asystem 100 designed according to these considerations and includes:storage manager 140, one or more data agents 142 executing on clientcomputing device(s) 102 and configured to process primary data 112, andone or more media agents 144 executing on one or more secondary storagecomputing devices 106 for performing tasks involving secondary storagedevices 108.

Storage Manager

Storage manager 140 is a centralized storage and/or information managerthat is configured to perform certain control functions and also tostore certain critical information about system 100—hence storagemanager 140 is said to manage system 100. As noted, the number ofcomponents in system 100 and the amount of data under management can belarge. Managing the components and data is therefore a significant task,which can grow unpredictably as the number of components and data scaleto meet the needs of the organization. For these and other reasons,according to certain embodiments, responsibility for controlling system100, or at least a significant portion of that responsibility, isallocated to storage manager 140. Storage manager 140 can be adaptedindependently according to changing circumstances, without having toreplace or re-design the remainder of the system. Moreover, a computingdevice for hosting and/or operating as storage manager 140 can beselected to best suit the functions and networking needs of storagemanager 140. These and other advantages are described in further detailbelow and with respect to FIG. 1D.

Storage manager 140 may be a software module or other application hostedby a suitable computing device. In some embodiments, storage manager 140is itself a computing device that performs the functions describedherein. Storage manager 140 comprises or operates in conjunction withone or more associated data structures such as a dedicated database(e.g., management database 146), depending on the configuration. Thestorage manager 140 generally initiates, performs, coordinates, and/orcontrols storage and other information management operations performedby system 100, e.g., to protect and control primary data 112 andsecondary copies 116. In general, storage manager 140 is said to managesystem 100, which includes communicating with, instructing, andcontrolling in some circumstances components such as data agents 142 andmedia agents 144, etc.

As shown by the dashed arrowed lines 114 in FIG. 1C, storage manager 140may communicate with, instruct, and/or control some or all elements ofsystem 100, such as data agents 142 and media agents 144. In thismanner, storage manager 140 manages the operation of various hardwareand software components in system 100. In certain embodiments, controlinformation originates from storage manager 140 and status as well asindex reporting is transmitted to storage manager 140 by the managedcomponents, whereas payload data and metadata are generally communicatedbetween data agents 142 and media agents 144 (or otherwise betweenclient computing device(s) 102 and secondary storage computing device(s)106), e.g., at the direction of and under the management of storagemanager 140. Control information can generally include parameters andinstructions for carrying out information management operations, suchas, without limitation, instructions to perform a task associated withan operation, timing information specifying when to initiate a task,data path information specifying what components to communicate with oraccess in carrying out an operation, and the like. In other embodiments,some information management operations are controlled or initiated byother components of system 100 (e.g., by media agents 144 or data agents142), instead of or in combination with storage manager 140.

According to certain embodiments, storage manager 140 provides one ormore of the following functions:

-   -   communicating with data agents 142 and media agents 144,        including transmitting instructions, messages, and/or queries,        as well as receiving status reports, index information,        messages, and/or queries, and responding to same;    -   initiating execution of information management operations;    -   initiating restore and recovery operations;    -   managing secondary storage devices 108 and inventory/capacity of        the same;    -   allocating secondary storage devices 108 for secondary copy        operations;    -   reporting, searching, and/or classification of data in system        100;    -   monitoring completion of and status reporting related to        information management operations and jobs;    -   tracking movement of data within system 100;    -   tracking age information relating to secondary copies 116,        secondary storage devices 108, comparing the age information        against retention guidelines, and initiating data pruning when        appropriate;    -   tracking logical associations between components in system 100;    -   protecting metadata associated with system 100, e.g., in        management database 146;    -   implementing job management, schedule management, event        management, alert management, reporting, job history        maintenance, user security management, disaster recovery        management, and/or user interfacing for system administrators        and/or end users of system 100;    -   sending, searching, and/or viewing of log files; and    -   implementing operations management functionality.

Storage manager 140 may maintain an associated database 146 (or “storagemanager database 146” or “management database 146”) ofmanagement-related data and information management policies 148.Database 146 is stored in computer memory accessible by storage manager140. Database 146 may include a management index 150 (or “index 150”) orother data structure(s) that may store: logical associations betweencomponents of the system; user preferences and/or profiles (e.g.,preferences regarding encryption, compression, or deduplication ofprimary data or secondary copies; preferences regarding the scheduling,type, or other aspects of secondary copy or other operations; mappingsof particular information management users or user accounts to certaincomputing devices or other components, etc.; management tasks; mediacontainerization; other useful data; and/or any combination thereof. Forexample, storage manager 140 may use index 150 to track logicalassociations between media agents 144 and secondary storage devices 108and/or movement of data to/from secondary storage devices 108. Forinstance, index 150 may store data associating a client computing device102 with a particular media agent 144 and/or secondary storage device108, as specified in an information management policy 148.

Administrators and others may configure and initiate certain informationmanagement operations on an individual basis. But while this may beacceptable for some recovery operations or other infrequent tasks, it isoften not workable for implementing on-going organization-wide dataprotection and management. Thus, system 100 may utilize informationmanagement policies 148 for specifying and executing informationmanagement operations on an automated basis. Generally, an informationmanagement policy 148 can include a stored data structure or otherinformation source that specifies parameters (e.g., criteria and rules)associated with storage management or other information managementoperations. Storage manager 140 can process an information managementpolicy 148 and/or index 150 and, based on the results, identify aninformation management operation to perform, identify the appropriatecomponents in system 100 to be involved in the operation (e.g., clientcomputing devices 102 and corresponding data agents 142, secondarystorage computing devices 106 and corresponding media agents 144, etc.),establish connections to those components and/or between thosecomponents, and/or instruct and control those components to carry outthe operation. In this manner, system 100 can translate storedinformation into coordinated activity among the various computingdevices in system 100.

Management database 146 may maintain information management policies 148and associated data, although information management policies 148 can bestored in computer memory at any appropriate location outside managementdatabase 146. For instance, an information management policy 148 such asa storage policy may be stored as metadata in a media agent database 152or in a secondary storage device 108 (e.g., as an archive copy) for usein restore or other information management operations, depending on theembodiment. Information management policies 148 are described furtherbelow. According to certain embodiments, management database 146comprises a relational database (e.g., an SQL database) for trackingmetadata, such as metadata associated with secondary copy operations(e.g., what client computing devices 102 and corresponding subclientdata were protected and where the secondary copies are stored and whichmedia agent 144 performed the storage operation(s)). This and othermetadata may additionally be stored in other locations, such as atsecondary storage computing device 106 or on the secondary storagedevice 108, allowing data recovery without the use of storage manager140 in some cases. Thus, management database 146 may comprise dataneeded to kick off secondary copy operations (e.g., storage policies,schedule policies, etc.), status and reporting information aboutcompleted jobs (e.g., status and error reports on yesterday's backupjobs), and additional information sufficient to enable restore anddisaster recovery operations (e.g., media agent associations, locationindexing, content indexing, etc.).

Storage manager 140 may include a jobs agent 156, a user interface 158,and a management agent 154, all of which may be implemented asinterconnected software modules or application programs. These aredescribed further below.

Jobs agent 156 in some embodiments initiates, controls, and/or monitorsthe status of some or all information management operations previouslyperformed, currently being performed, or scheduled to be performed bysystem 100. A job is a logical grouping of information managementoperations such as daily storage operations scheduled for a certain setof subclients (e.g., generating incremental block-level backup copies116 at a certain time every day for database files in a certaingeographical location). Thus, jobs agent 156 may access informationmanagement policies 148 (e.g., in management database 146) to determinewhen, where, and how to initiate/control jobs in system 100.

Storage Manager User Interfaces

User interface 158 may include information processing and displaysoftware, such as a graphical user interface (GUI), an applicationprogram interface (API), and/or other interactive interface(s) throughwhich users and system processes can retrieve information about thestatus of information management operations or issue instructions tostorage manager 140 and other components. Via user interface 158, usersmay issue instructions to the components in system 100 regardingperformance of secondary copy and recovery operations. For example, auser may modify a schedule concerning the number of pending secondarycopy operations. As another example, a user may employ the GUI to viewthe status of pending secondary copy jobs or to monitor the status ofcertain components in system 100 (e.g., the amount of capacity left in astorage device). Storage manager 140 may track information that permitsit to select, designate, or otherwise identify content indices,deduplication databases, or similar databases or resources or data setswithin its information management cell (or another cell) to be searchedin response to certain queries. Such queries may be entered by the userby interacting with user interface 158.

Various embodiments of information management system 100 may beconfigured and/or designed to generate user interface data usable forrendering the various interactive user interfaces described. The userinterface data may be used by system 100 and/or by another system,device, and/or software program (for example, a browser program), torender the interactive user interfaces. The interactive user interfacesmay be displayed on, for example, electronic displays (including, forexample, touch-enabled displays), consoles, etc., whetherdirect-connected to storage manager 140 or communicatively coupledremotely, e.g., via an internet connection. The present disclosuredescribes various embodiments of interactive and dynamic userinterfaces, some of which may be generated by user interface agent 158,and which are the result of significant technological development. Theuser interfaces described herein may provide improved human-computerinteractions, allowing for significant cognitive and ergonomicefficiencies and advantages over previous systems, including reducedmental workloads, improved decision-making, and the like. User interface158 may operate in a single integrated view or console (not shown). Theconsole may support a reporting capability for generating a variety ofreports, which may be tailored to a particular aspect of informationmanagement.

User interfaces are not exclusive to storage manager 140 and in someembodiments a user may access information locally from a computingdevice component of system 100. For example, some information pertainingto installed data agents 142 and associated data streams may beavailable from client computing device 102. Likewise, some informationpertaining to media agents 144 and associated data streams may beavailable from secondary storage computing device 106.

Storage Manager Management Agent

Management agent 154 can provide storage manager 140 with the ability tocommunicate with other components within system 100 and/or with otherinformation management cells via network protocols and applicationprogramming interfaces (APIs) including, e.g., HTTP, HTTPS, FTP, REST,virtualization software APIs, cloud service provider APIs, and hostedservice provider APIs, without limitation. Management agent 154 alsoallows multiple information management cells to communicate with oneanother. For example, system 100 in some cases may be one informationmanagement cell in a network of multiple cells adjacent to one anotheror otherwise logically related, e.g., in a WAN or LAN. With thisarrangement, the cells may communicate with one another throughrespective management agents 154. Inter-cell communications andhierarchy is described in greater detail in e.g., U.S. Pat. No.7,343,453.

Information Management Cell

An “information management cell” (or “storage operation cell” or “cell”)may generally include a logical and/or physical grouping of acombination of hardware and software components associated withperforming information management operations on electronic data,typically one storage manager 140 and at least one data agent 142(executing on a client computing device 102) and at least one mediaagent 144 (executing on a secondary storage computing device 106). Forinstance, the components shown in FIG. 1C may together form aninformation management cell. Thus, in some configurations, a system 100may be referred to as an information management cell or a storageoperation cell. A given cell may be identified by the identity of itsstorage manager 140, which is generally responsible for managing thecell.

Multiple cells may be organized hierarchically, so that cells mayinherit properties from hierarchically superior cells or be controlledby other cells in the hierarchy (automatically or otherwise).Alternatively, in some embodiments, cells may inherit or otherwise beassociated with information management policies, preferences,information management operational parameters, or other properties orcharacteristics according to their relative position in a hierarchy ofcells. Cells may also be organized hierarchically according to function,geography, architectural considerations, or other factors useful ordesirable in performing information management operations. For example,a first cell may represent a geographic segment of an enterprise, suchas a Chicago office, and a second cell may represent a differentgeographic segment, such as a New York City office. Other cells mayrepresent departments within a particular office, e.g., human resources,finance, engineering, etc. Where delineated by function, a first cellmay perform one or more first types of information management operations(e.g., one or more first types of secondary copies at a certainfrequency), and a second cell may perform one or more second types ofinformation management operations (e.g., one or more second types ofsecondary copies at a different frequency and under different retentionrules). In general, the hierarchical information is maintained by one ormore storage managers 140 that manage the respective cells (e.g., incorresponding management database(s) 146).

Data Agents

A variety of different applications 110 can operate on a given clientcomputing device 102, including operating systems, file systems,database applications, e-mail applications, and virtual machines, justto name a few. And, as part of the process of creating and restoringsecondary copies 116, the client computing device 102 may be tasked withprocessing and preparing the primary data 112 generated by these variousapplications 110. Moreover, the nature of the processing/preparation candiffer across application types, e.g., due to inherent structural,state, and formatting differences among applications 110 and/or theoperating system of client computing device 102. Each data agent 142 istherefore advantageously configured in some embodiments to assist in theperformance of information management operations based on the type ofdata that is being protected at a client-specific and/orapplication-specific level.

Data agent 142 is a component of information system 100 and is generallydirected by storage manager 140 to participate in creating or restoringsecondary copies 116. Data agent 142 may be a software program (e.g., inthe form of a set of executable binary files) that executes on the sameclient computing device 102 as the associated application 110 that dataagent 142 is configured to protect. Data agent 142 is generallyresponsible for managing, initiating, or otherwise assisting in theperformance of information management operations in reference to itsassociated application(s) 110 and corresponding primary data 112 whichis generated/accessed by the particular application(s) 110. Forinstance, data agent 142 may take part in copying, archiving, migrating,and/or replicating of certain primary data 112 stored in the primarystorage device(s) 104. Data agent 142 may receive control informationfrom storage manager 140, such as commands to transfer copies of dataobjects and/or metadata to one or more media agents 144. Data agent 142also may compress, deduplicate, and encrypt certain primary data 112, aswell as capture application-related metadata before transmitting theprocessed data to media agent 144. Data agent 142 also may receiveinstructions from storage manager 140 to restore (or assist inrestoring) a secondary copy 116 from secondary storage device 108 toprimary storage 104, such that the restored data may be properlyaccessed by application 110 in a suitable format as though it wereprimary data 112.

Each data agent 142 may be specialized for a particular application 110.For instance, different individual data agents 142 may be designed tohandle Microsoft Exchange data, Lotus Notes data, Microsoft Windows filesystem data, Microsoft Active Directory Objects data, SQL Server data,Share Point data, Oracle database data, SAP database data, virtualmachines and/or associated data, and other types of data. A file systemdata agent, for example, may handle data files and/or other file systeminformation. If a client computing device 102 has two or more types ofdata 112, a specialized data agent 142 may be used for each data type.For example, to backup, migrate, and/or restore all of the data on aMicrosoft Exchange server, the client computing device 102 may use: (1)a Microsoft Exchange Mailbox data agent 142 to back up the Exchangemailboxes; (2) a Microsoft Exchange Database data agent 142 to back upthe Exchange databases; (3) a Microsoft Exchange Public Folder dataagent 142 to back up the Exchange Public Folders; and (4) a MicrosoftWindows File System data agent 142 to back up the file system of clientcomputing device 102. In this example, these specialized data agents 142are treated as four separate data agents 142 even though they operate onthe same client computing device 102. Other examples may include archivemanagement data agents such as a migration archiver or a compliancearchiver, Quick Recovery® agents, and continuous data replicationagents. Application-specific data agents 142 can provide improvedperformance as compared to generic agents. For instance, becauseapplication-specific data agents 142 may only handle data for a singlesoftware application, the design, operation, and performance of the dataagent 142 can be streamlined. The data agent 142 may therefore executefaster and consume less persistent storage and/or operating memory thandata agents designed to generically accommodate multiple differentsoftware applications 110.

Each data agent 142 may be configured to access data and/or metadatastored in the primary storage device(s) 104 associated with data agent142 and its host client computing device 102, and process the dataappropriately. For example, during a secondary copy operation, dataagent 142 may arrange or assemble the data and metadata into one or morefiles having a certain format (e.g., a particular backup or archiveformat) before transferring the file(s) to a media agent 144 or othercomponent. The file(s) may include a list of files or other metadata. Insome embodiments, a data agent 142 may be distributed between clientcomputing device 102 and storage manager 140 (and any other intermediatecomponents) or may be deployed from a remote location or its functionsapproximated by a remote process that performs some or all of thefunctions of data agent 142. In addition, a data agent 142 may performsome functions provided by media agent 144. Other embodiments may employone or more generic data agents 142 that can handle and process datafrom two or more different applications 110, or that can handle andprocess multiple data types, instead of or in addition to usingspecialized data agents 142. For example, one generic data agent 142 maybe used to back up, migrate and restore Microsoft Exchange Mailbox dataand Microsoft Exchange Database data, while another generic data agentmay handle Microsoft Exchange Public Folder data and Microsoft WindowsFile System data.

Media Agents

As noted, off-loading certain responsibilities from client computingdevices 102 to intermediate components such as secondary storagecomputing device(s) 106 and corresponding media agent(s) 144 can providea number of benefits including improved performance of client computingdevice 102, faster and more reliable information management operations,and enhanced scalability. In one example which will be discussed furtherbelow, media agent 144 can act as a local cache of recently-copied dataand/or metadata stored to secondary storage device(s) 108, thusimproving restore capabilities and performance for the cached data.

Media agent 144 is a component of system 100 and is generally directedby storage manager 140 in creating and restoring secondary copies 116.Whereas storage manager 140 generally manages system 100 as a whole,media agent 144 provides a portal to certain secondary storage devices108, such as by having specialized features for communicating with andaccessing certain associated secondary storage device 108. Media agent144 may be a software program (e.g., in the form of a set of executablebinary files) that executes on a secondary storage computing device 106.Media agent 144 generally manages, coordinates, and facilitates thetransmission of data between a data agent 142 (executing on clientcomputing device 102) and secondary storage device(s) 108 associatedwith media agent 144. For instance, other components in the system mayinteract with media agent 144 to gain access to data stored onassociated secondary storage device(s) 108, (e.g., to browse, read,write, modify, delete, or restore data). Moreover, media agents 144 cangenerate and store information relating to characteristics of the storeddata and/or metadata, or can generate and store other types ofinformation that generally provides insight into the contents of thesecondary storage devices 108—generally referred to as indexing of thestored secondary copies 116. Each media agent 144 may operate on adedicated secondary storage computing device 106, while in otherembodiments a plurality of media agents 144 may operate on the samesecondary storage computing device 106.

A media agent 144 may be associated with a particular secondary storagedevice 108 if that media agent 144 is capable of one or more of: routingand/or storing data to the particular secondary storage device 108;coordinating the routing and/or storing of data to the particularsecondary storage device 108; retrieving data from the particularsecondary storage device 108; coordinating the retrieval of data fromthe particular secondary storage device 108; and modifying and/ordeleting data retrieved from the particular secondary storage device108. Media agent 144 in certain embodiments is physically separate fromthe associated secondary storage device 108. For instance, a media agent144 may operate on a secondary storage computing device 106 in adistinct housing, package, and/or location from the associated secondarystorage device 108. In one example, a media agent 144 operates on afirst server computer and is in communication with a secondary storagedevice(s) 108 operating in a separate rack-mounted RAID-based system.

A media agent 144 associated with a particular secondary storage device108 may instruct secondary storage device 108 to perform an informationmanagement task. For instance, a media agent 144 may instruct a tapelibrary to use a robotic arm or other retrieval means to load or eject acertain storage media, and to subsequently archive, migrate, or retrievedata to or from that media, e.g., for the purpose of restoring data to aclient computing device 102. As another example, a secondary storagedevice 108 may include an array of hard disk drives or solid statedrives organized in a RAID configuration, and media agent 144 mayforward a logical unit number (LUN) and other appropriate information tothe array, which uses the received information to execute the desiredsecondary copy operation. Media agent 144 may communicate with asecondary storage device 108 via a suitable communications link, such asa SCSI or Fibre Channel link.

Each media agent 144 may maintain an associated media agent database152. Media agent database 152 may be stored to a disk or other storagedevice (not shown) that is local to the secondary storage computingdevice 106 on which media agent 144 executes. In other cases, mediaagent database 152 is stored separately from the host secondary storagecomputing device 106. Media agent database 152 can include, among otherthings, a media agent index 153 (see, e.g., FIG. 1C). In some cases,media agent index 153 does not form a part of and is instead separatefrom media agent database 152.

Media agent index 153 (or “index 153”) may be a data structureassociated with the particular media agent 144 that includes informationabout the stored data associated with the particular media agent andwhich may be generated in the course of performing a secondary copyoperation or a restore. Index 153 provides a fast and efficientmechanism for locating/browsing secondary copies 116 or other datastored in secondary storage devices 108 without having to accesssecondary storage device 108 to retrieve the information from there. Forinstance, for each secondary copy 116, index 153 may include metadatasuch as a list of the data objects (e.g., files/subdirectories, databaseobjects, mailbox objects, etc.), a logical path to the secondary copy116 on the corresponding secondary storage device 108, locationinformation (e.g., offsets) indicating where the data objects are storedin the secondary storage device 108, when the data objects were createdor modified, etc. Thus, index 153 includes metadata associated with thesecondary copies 116 that is readily available for use from media agent144. In some embodiments, some or all of the information in index 153may instead or additionally be stored along with secondary copies 116 insecondary storage device 108. In some embodiments, a secondary storagedevice 108 can include sufficient information to enable a “bare metalrestore,” where the operating system and/or software applications of afailed client computing device 102 or another target may beautomatically restored without manually reinstalling individual softwarepackages (including operating systems).

Because index 153 may operate as a cache, it can also be referred to asan “index cache.” In such cases, information stored in index cache 153typically comprises data that reflects certain particulars aboutrelatively recent secondary copy operations. After some triggeringevent, such as after some time elapses or index cache 153 reaches aparticular size, certain portions of index cache 153 may be copied ormigrated to secondary storage device 108, e.g., on a least-recently-usedbasis. This information may be retrieved and uploaded back into indexcache 153 or otherwise restored to media agent 144 to facilitateretrieval of data from the secondary storage device(s) 108. In someembodiments, the cached information may include format orcontainerization information related to archives or other files storedon storage device(s) 108.

In some alternative embodiments media agent 144 generally acts as acoordinator or facilitator of secondary copy operations between clientcomputing devices 102 and secondary storage devices 108, but does notactually write the data to secondary storage device 108. For instance,storage manager 140 (or media agent 144) may instruct a client computingdevice 102 and secondary storage device 108 to communicate with oneanother directly. In such a case, client computing device 102 transmitsdata directly or via one or more intermediary components to secondarystorage device 108 according to the received instructions, and viceversa. Media agent 144 may still receive, process, and/or maintainmetadata related to the secondary copy operations, i.e., may continue tobuild and maintain index 153. In these embodiments, payload data canflow through media agent 144 for the purposes of populating index 153,but not for writing to secondary storage device 108. Media agent 144and/or other components such as storage manager 140 may in some casesincorporate additional functionality, such as data classification,content indexing, deduplication, encryption, compression, and the like.Further details regarding these and other functions are described below.

Distributed, Scalable Architecture

As described, certain functions of system 100 can be distributed amongstvarious physical and/or logical components. For instance, one or more ofstorage manager 140, data agents 142, and media agents 144 may operateon computing devices that are physically separate from one another. Thisarchitecture can provide a number of benefits. For instance, hardwareand software design choices for each distributed component can betargeted to suit its particular function. The secondary computingdevices 106 on which media agents 144 operate can be tailored forinteraction with associated secondary storage devices 108 and providefast index cache operation, among other specific tasks. Similarly,client computing device(s) 102 can be selected to effectively serviceapplications 110 in order to efficiently produce and store primary data112.

Moreover, in some cases, one or more of the individual components ofinformation management system 100 can be distributed to multipleseparate computing devices. As one example, for large file systems wherethe amount of data stored in management database 146 is relativelylarge, database 146 may be migrated to or may otherwise reside on aspecialized database server (e.g., an SQL server) separate from a serverthat implements the other functions of storage manager 140. Thisdistributed configuration can provide added protection because database146 can be protected with standard database utilities (e.g., SQL logshipping or database replication) independent from other functions ofstorage manager 140. Database 146 can be efficiently replicated to aremote site for use in the event of a disaster or other data loss at theprimary site. Or database 146 can be replicated to another computingdevice within the same site, such as to a higher performance machine inthe event that a storage manager host computing device can no longerservice the needs of a growing system 100.

The distributed architecture also provides scalability and efficientcomponent utilization. FIG. 1D shows an embodiment of informationmanagement system 100 including a plurality of client computing devices102 and associated data agents 142 as well as a plurality of secondarystorage computing devices 106 and associated media agents 144.Additional components can be added or subtracted based on the evolvingneeds of system 100. For instance, depending on where bottlenecks areidentified, administrators can add additional client computing devices102, secondary storage computing devices 106, and/or secondary storagedevices 108. Moreover, where multiple fungible components are available,load balancing can be implemented to dynamically address identifiedbottlenecks. As an example, storage manager 140 may dynamically selectwhich media agents 144 and/or secondary storage devices 108 to use forstorage operations based on a processing load analysis of media agents144 and/or secondary storage devices 108, respectively.

Where system 100 includes multiple media agents 144 (see, e.g., FIG.1D), a first media agent 144 may provide failover functionality for asecond failed media agent 144. In addition, media agents 144 can bedynamically selected to provide load balancing. Each client computingdevice 102 can communicate with, among other components, any of themedia agents 144, e.g., as directed by storage manager 140. And eachmedia agent 144 may communicate with, among other components, any ofsecondary storage devices 108, e.g., as directed by storage manager 140.Thus, operations can be routed to secondary storage devices 108 in adynamic and highly flexible manner, to provide load balancing, failover,etc. Further examples of scalable systems capable of dynamic storageoperations, load balancing, and failover are provided in U.S. Pat. No.7,246,207.

While distributing functionality amongst multiple computing devices canhave certain advantages, in other contexts it can be beneficial toconsolidate functionality on the same computing device. In alternativeconfigurations, certain components may reside and execute on the samecomputing device. As such, in other embodiments, one or more of thecomponents shown in FIG. 1C may be implemented on the same computingdevice. In one configuration, a storage manager 140, one or more dataagents 142, and/or one or more media agents 144 are all implemented onthe same computing device. In other embodiments, one or more data agents142 and one or more media agents 144 are implemented on the samecomputing device, while storage manager 140 is implemented on a separatecomputing device, etc. without limitation.

Exemplary Types of Information Management Operations, Including StorageOperations

In order to protect and leverage stored data, system 100 can beconfigured to perform a variety of information management operations,which may also be referred to in some cases as storage managementoperations or storage operations. These operations can generally include(i) data movement operations, (ii) processing and data manipulationoperations, and (iii) analysis, reporting, and management operations.

Data Movement Operations, Including Secondary Copy Operations

Data movement operations are generally storage operations that involvethe copying or migration of data between different locations in system100. For example, data movement operations can include operations inwhich stored data is copied, migrated, or otherwise transferred from oneor more first storage devices to one or more second storage devices,such as from primary storage device(s) 104 to secondary storagedevice(s) 108, from secondary storage device(s) 108 to differentsecondary storage device(s) 108, from secondary storage devices 108 toprimary storage devices 104, or from primary storage device(s) 104 todifferent primary storage device(s) 104, or in some cases within thesame primary storage device 104 such as within a storage array.

Data movement operations can include by way of example, backupoperations, archive operations, information lifecycle managementoperations such as hierarchical storage management operations,replication operations (e.g., continuous data replication), snapshotoperations, deduplication or single-instancing operations, auxiliarycopy operations, disaster-recovery copy operations, and the like. Aswill be discussed, some of these operations do not necessarily createdistinct copies. Nonetheless, some or all of these operations aregenerally referred to as “secondary copy operations” for simplicity,because they involve secondary copies. Data movement also comprisesrestoring secondary copies.

Backup Operations

A backup operation creates a copy of a version of primary data 112 at aparticular point in time (e.g., one or more files or other data units).Each subsequent backup copy 116 (which is a form of secondary copy 116)may be maintained independently of the first. A backup generallyinvolves maintaining a version of the copied primary data 112 as well asbackup copies 116. Further, a backup copy in some embodiments isgenerally stored in a form that is different from the native format,e.g., a backup format. This contrasts to the version in primary data 112which may instead be stored in a format native to the sourceapplication(s) 110. In various cases, backup copies can be stored in aformat in which the data is compressed, encrypted, deduplicated, and/orotherwise modified from the original native application format. Forexample, a backup copy may be stored in a compressed backup format thatfacilitates efficient long-term storage. Backup copies 116 can haverelatively long retention periods as compared to primary data 112, whichis generally highly changeable. Backup copies 116 may be stored on mediawith slower retrieval times than primary storage device 104. Some backupcopies may have shorter retention periods than some other types ofsecondary copies 116, such as archive copies (described below). Backupsmay be stored at an offsite location.

Backup operations can include full backups, differential backups,incremental backups, “synthetic full” backups, and/or creating a“reference copy.” A full backup (or “standard full backup”) in someembodiments is generally a complete image of the data to be protected.However, because full backup copies can consume a relatively largeamount of storage, it can be useful to use a full backup copy as abaseline and only store changes relative to the full backup copyafterwards.

A differential backup operation (or cumulative incremental backupoperation) tracks and stores changes that occurred since the last fullbackup. Differential backups can grow quickly in size, but can restorerelatively efficiently because a restore can be completed in some casesusing only the full backup copy and the latest differential copy.

An incremental backup operation generally tracks and stores changessince the most recent backup copy of any type, which can greatly reducestorage utilization. In some cases, however, restoring can be lengthycompared to full or differential backups because completing a restoreoperation may involve accessing a full backup in addition to multipleincremental backups.

Synthetic full backups generally consolidate data without directlybacking up data from the client computing device. A synthetic fullbackup is created from the most recent full backup (i.e., standard orsynthetic) and subsequent incremental and/or differential backups. Theresulting synthetic full backup is identical to what would have beencreated had the last backup for the subclient been a standard fullbackup. Unlike standard full, incremental, and differential backups,however, a synthetic full backup does not actually transfer data fromprimary storage to the backup media, because it operates as a backupconsolidator. A synthetic full backup extracts the index data of eachparticipating subclient. Using this index data and the previously backedup user data images, it builds new full backup images (e.g., bitmaps),one for each subclient. The new backup images consolidate the index anduser data stored in the related incremental, differential, and previousfull backups into a synthetic backup file that fully represents thesubclient (e.g., via pointers) but does not comprise all its constituentdata.

Any of the above types of backup operations can be at the volume level,file level, or block level. Volume level backup operations generallyinvolve copying of a data volume (e.g., a logical disk or partition) asa whole. In a file-level backup, information management system 100generally tracks changes to individual files and includes copies offiles in the backup copy. For block-level backups, files are broken intoconstituent blocks, and changes are tracked at the block level. Uponrestore, system 100 reassembles the blocks into files in a transparentfashion. Far less data may actually be transferred and copied tosecondary storage devices 108 during a file-level copy than avolume-level copy. Likewise, a block-level copy may transfer less datathan a file-level copy, resulting in faster execution. However,restoring a relatively higher-granularity copy can result in longerrestore times. For instance, when restoring a block-level copy, theprocess of locating and retrieving constituent blocks can sometimes takelonger than restoring file-level backups.

A reference copy may comprise copy(ies) of selected objects from backedup data, typically to help organize data by keeping contextualinformation from multiple sources together, and/or help retain specificdata for a longer period of time, such as for legal hold needs. Areference copy generally maintains data integrity, and when the data isrestored, it may be viewed in the same format as the source data. Insome embodiments, a reference copy is based on a specialized client,individual subclient and associated information management policies(e.g., storage policy, retention policy, etc.) that are administeredwithin system 100.

Archive Operations

Because backup operations generally involve maintaining a version of thecopied primary data 112 and also maintaining backup copies in secondarystorage device(s) 108, they can consume significant storage capacity. Toreduce storage consumption, an archive operation according to certainembodiments creates an archive copy 116 by both copying and removingsource data. Or, seen another way, archive operations can involve movingsome or all of the source data to the archive destination. Thus, datasatisfying criteria for removal (e.g., data of a threshold age or size)may be removed from source storage. The source data may be primary data112 or a secondary copy 116, depending on the situation. As with backupcopies, archive copies can be stored in a format in which the data iscompressed, encrypted, deduplicated, and/or otherwise modified from theformat of the original application or source copy. In addition, archivecopies may be retained for relatively long periods of time (e.g., years)and, in some cases are never deleted. In certain embodiments, archivecopies may be made and kept for extended periods in order to meetcompliance regulations.

Archiving can also serve the purpose of freeing up space in primarystorage device(s) 104 and easing the demand on computational resourceson client computing device 102. Similarly, when a secondary copy 116 isarchived, the archive copy can therefore serve the purpose of freeing upspace in the source secondary storage device(s) 108. Examples of dataarchiving operations are provided in U.S. Pat. No. 7,107,298.

Snapshot Operations

Snapshot operations can provide a relatively lightweight, efficientmechanism for protecting data. From an end-user viewpoint, a snapshotmay be thought of as an “instant” image of primary data 112 at a givenpoint in time, and may include state and/or status information relativeto an application 110 that creates/manages primary data 112. In oneembodiment, a snapshot may generally capture the directory structure ofan object in primary data 112 such as a file or volume or other data setat a particular moment in time and may also preserve file attributes andcontents. A snapshot in some cases is created relatively quickly, e.g.,substantially instantly, using a minimum amount of file space, but maystill function as a conventional file system backup.

A “hardware snapshot” (or “hardware-based snapshot”) operation occurswhere a target storage device (e.g., a primary storage device 104 or asecondary storage device 108) performs the snapshot operation in aself-contained fashion, substantially independently, using hardware,firmware and/or software operating on the storage device itself. Forinstance, the storage device may perform snapshot operations generallywithout intervention or oversight from any of the other components ofthe system 100, e.g., a storage array may generate an “array-created”hardware snapshot and may also manage its storage, integrity,versioning, etc. In this manner, hardware snapshots can off-load othercomponents of system 100 from snapshot processing. An array may receivea request from another component to take a snapshot and then proceed toexecute the “hardware snapshot” operations autonomously, preferablyreporting success to the requesting component.

A “software snapshot” (or “software-based snapshot”) operation, on theother hand, occurs where a component in system 100 (e.g., clientcomputing device 102, etc.) implements a software layer that manages thesnapshot operation via interaction with the target storage device. Forinstance, the component executing the snapshot management software layermay derive a set of pointers and/or data that represents the snapshot.The snapshot management software layer may then transmit the same to thetarget storage device, along with appropriate instructions for writingthe snapshot. One example of a software snapshot product is MicrosoftVolume Snapshot Service (VSS), which is part of the Microsoft Windowsoperating system.

Some types of snapshots do not actually create another physical copy ofall the data as it existed at the particular point in time, but maysimply create pointers that map files and directories to specific memorylocations (e.g., to specific disk blocks) where the data resides as itexisted at the particular point in time. For example, a snapshot copymay include a set of pointers derived from the file system or from anapplication. In some other cases, the snapshot may be created at theblock-level, such that creation of the snapshot occurs without awarenessof the file system. Each pointer points to a respective stored datablock, so that collectively, the set of pointers reflect the storagelocation and state of the data object (e.g., file(s) or volume(s) ordata set(s)) at the point in time when the snapshot copy was created.

An initial snapshot may use only a small amount of disk space needed torecord a mapping or other data structure representing or otherwisetracking the blocks that correspond to the current state of the filesystem. Additional disk space is usually required only when files anddirectories change later on. Furthermore, when files change, typicallyonly the pointers which map to blocks are copied, not the blocksthemselves. For example for “copy-on-write” snapshots, when a blockchanges in primary storage, the block is copied to secondary storage orcached in primary storage before the block is overwritten in primarystorage, and the pointer to that block is changed to reflect the newlocation of that block. The snapshot mapping of file system data mayalso be updated to reflect the changed block(s) at that particular pointin time. In some other cases, a snapshot includes a full physical copyof all or substantially all of the data represented by the snapshot.Further examples of snapshot operations are provided in U.S. Pat. No.7,529,782. A snapshot copy in many cases can be made quickly and withoutsignificantly impacting primary computing resources because largeamounts of data need not be copied or moved. In some embodiments, asnapshot may exist as a virtual file system, parallel to the actual filesystem. Users in some cases gain read-only access to the record of filesand directories of the snapshot. By electing to restore primary data 112from a snapshot taken at a given point in time, users may also returnthe current file system to the state of the file system that existedwhen the snapshot was taken.

Replication Operations

Replication is another type of secondary copy operation. Some types ofsecondary copies 116 periodically capture images of primary data 112 atparticular points in time (e.g., backups, archives, and snapshots).However, it can also be useful for recovery purposes to protect primarydata 112 in a more continuous fashion, by replicating primary data 112substantially as changes occur. In some cases a replication copy can bea mirror copy, for instance, where changes made to primary data 112 aremirrored or substantially immediately copied to another location (e.g.,to secondary storage device(s) 108). By copying each write operation tothe replication copy, two storage systems are kept synchronized orsubstantially synchronized so that they are virtually identical atapproximately the same time. Where entire disk volumes are mirrored,however, mirroring can require significant amount of storage space andutilizes a large amount of processing resources.

According to some embodiments, secondary copy operations are performedon replicated data that represents a recoverable state, or “known goodstate” of a particular application running on the source system. Forinstance, in certain embodiments, known good replication copies may beviewed as copies of primary data 112. This feature allows the system todirectly access, copy, restore, back up, or otherwise manipulate thereplication copies as if they were the “live” primary data 112. This canreduce access time, storage utilization, and impact on sourceapplications 110, among other benefits. Based on known good stateinformation, system 100 can replicate sections of application data thatrepresent a recoverable state rather than rote copying of blocks ofdata. Examples of replication operations (e.g., continuous datareplication) are provided in U.S. Pat. No. 7,617,262.

Deduplication/Single-Instancing Operations

Deduplication or single-instance storage is useful to reduce the amountof non-primary data. For instance, some or all of the above-describedsecondary copy operations can involve deduplication in some fashion. Newdata is read, broken down into data portions of a selected granularity(e.g., sub-file level blocks, files, etc.), compared with correspondingportions that are already in secondary storage, and only new/changedportions are stored. Portions that already exist are represented aspointers to the already-stored data. Thus, a deduplicated secondary copy116 may comprise actual data portions copied from primary data 112 andmay further comprise pointers to already-stored data, which is generallymore storage-efficient than a full copy.

In order to streamline the comparison process, system 100 may calculateand/or store signatures (e.g., hashes or cryptographically unique IDs)corresponding to the individual source data portions and compare thesignatures to already-stored data signatures, instead of comparingentire data portions. In some cases, only a single instance of each dataportion is stored, and deduplication operations may therefore bereferred to interchangeably as “single-instancing” operations. Dependingon the implementation, however, deduplication operations can store morethan one instance of certain data portions, yet still significantlyreduce stored-data redundancy. Depending on the embodiment,deduplication portions such as data blocks can be of fixed or variablelength. Using variable length blocks can enhance deduplication byresponding to changes in the data stream, but can involve more complexprocessing. In some cases, system 100 utilizes a technique fordynamically aligning deduplication blocks based on changing content inthe data stream, as described in U.S. Pat. No. 8,364,652.

System 100 can deduplicate in a variety of manners at a variety oflocations. For instance, in some embodiments, system 100 implements“target-side” deduplication by deduplicating data at the media agent 144after being received from data agent 142. In some such cases, mediaagents 144 are generally configured to manage the deduplication process.For instance, one or more of the media agents 144 maintain acorresponding deduplication database that stores deduplicationinformation (e.g., data block signatures). Examples of such aconfiguration are provided in U.S. Pat. No. 9,020,900. Instead of or incombination with “target-side” deduplication, “source-side” (or“client-side”) deduplication can also be performed, e.g., to reduce theamount of data to be transmitted by data agent 142 to media agent 144.Storage manager 140 may communicate with other components within system100 via network protocols and cloud service provider APIs to facilitatecloud-based deduplication/single instancing, as exemplified in U.S. Pat.No. 8,954,446. Some other deduplication/single instancing techniques aredescribed in U.S. Pat. Pub. No. 2006/0224846 and in U.S. Pat. No.9,098,495.

Information Lifecycle Management and Hierarchical Storage Management

In some embodiments, files and other data over their lifetime move frommore expensive quick-access storage to less expensive slower-accessstorage. Operations associated with moving data through various tiers ofstorage are sometimes referred to as information lifecycle management(ILM) operations.

One type of ILM operation is a hierarchical storage management (HSM)operation, which generally automatically moves data between classes ofstorage devices, such as from high-cost to low-cost storage devices. Forinstance, an HSM operation may involve movement of data from primarystorage devices 104 to secondary storage devices 108, or between tiersof secondary storage devices 108. With each tier, the storage devicesmay be progressively cheaper, have relatively slower access/restoretimes, etc. For example, movement of data between tiers may occur asdata becomes less important over time. In some embodiments, an HSMoperation is similar to archiving in that creating an HSM copy may(though not always) involve deleting some of the source data, e.g.,according to one or more criteria related to the source data. Forexample, an HSM copy may include primary data 112 or a secondary copy116 that exceeds a given size threshold or a given age threshold. Often,and unlike some types of archive copies, HSM data that is removed oraged from the source is replaced by a logical reference pointer or stub.The reference pointer or stub can be stored in the primary storagedevice 104 or other source storage device, such as a secondary storagedevice 108 to replace the deleted source data and to point to orotherwise indicate the new location in (another) secondary storagedevice 108.

For example, files are generally moved between higher and lower coststorage depending on how often the files are accessed. When a userrequests access to HSM data that has been removed or migrated, system100 uses the stub to locate the data and may make recovery of the dataappear transparent, even though the HSM data may be stored at a locationdifferent from other source data. In this manner, the data appears tothe user (e.g., in file system browsing windows and the like) as if itstill resides in the source location (e.g., in a primary storage device104). The stub may include metadata associated with the correspondingdata, so that a file system and/or application can provide someinformation about the data object and/or a limited-functionality version(e.g., a preview) of the data object.

An HSM copy may be stored in a format other than the native applicationformat (e.g., compressed, encrypted, deduplicated, and/or otherwisemodified). In some cases, copies which involve the removal of data fromsource storage and the maintenance of stub or other logical referenceinformation on source storage may be referred to generally as “onlinearchive copies.” On the other hand, copies which involve the removal ofdata from source storage without the maintenance of stub or otherlogical reference information on source storage may be referred to as“off-line archive copies.” Examples of HSM and ILM techniques areprovided in U.S. Pat. No. 7,343,453.

Auxiliary Copy Operations

An auxiliary copy is generally a copy of an existing secondary copy 116.For instance, an initial secondary copy 116 may be derived from primarydata 112 or from data residing in secondary storage subsystem 118,whereas an auxiliary copy is generated from the initial secondary copy116. Auxiliary copies provide additional standby copies of data and mayreside on different secondary storage devices 108 than the initialsecondary copies 116. Thus, auxiliary copies can be used for recoverypurposes if initial secondary copies 116 become unavailable. Exemplaryauxiliary copy techniques are described in further detail in U.S. Pat.No. 8,230,195.

Disaster-Recovery Copy Operations

System 100 may also make and retain disaster recovery copies, often assecondary, high-availability disk copies. System 100 may createsecondary copies and store them at disaster recovery locations usingauxiliary copy or replication operations, such as continuous datareplication technologies. Depending on the particular data protectiongoals, disaster recovery locations can be remote from the clientcomputing devices 102 and primary storage devices 104, remote from someor all of the secondary storage devices 108, or both.

Data Manipulation, Including Encryption and Compression

Data manipulation and processing may include encryption and compressionas well as integrity marking and checking, formatting for transmission,formatting for storage, etc. Data may be manipulated “client-side” bydata agent 142 as well as “target-side” by media agent 144 in the courseof creating secondary copy 116, or conversely in the course of restoringdata from secondary to primary.

Encryption Operations

System 100 in some cases is configured to process data (e.g., files orother data objects, primary data 112, secondary copies 116, etc.),according to an appropriate encryption algorithm (e.g., Blowfish,Advanced Encryption Standard (AES), Triple Data Encryption Standard(3-DES), etc.) to limit access and provide data security. System 100 insome cases encrypts the data at the client level, such that clientcomputing devices 102 (e.g., data agents 142) encrypt the data prior totransferring it to other components, e.g., before sending the data tomedia agents 144 during a secondary copy operation. In such cases,client computing device 102 may maintain or have access to an encryptionkey or passphrase for decrypting the data upon restore. Encryption canalso occur when media agent 144 creates auxiliary copies or archivecopies. Encryption may be applied in creating a secondary copy 116 of apreviously unencrypted secondary copy 116, without limitation. Infurther embodiments, secondary storage devices 108 can implementbuilt-in, high performance hardware-based encryption.

Compression Operations

Similar to encryption, system 100 may also or alternatively compressdata in the course of generating a secondary copy 116. Compressionencodes information such that fewer bits are needed to represent theinformation as compared to the original representation. Compressiontechniques are well known in the art. Compression operations may applyone or more data compression algorithms. Compression may be applied increating a secondary copy 116 of a previously uncompressed secondarycopy, e.g., when making archive copies or disaster recovery copies. Theuse of compression may result in metadata that specifies the nature ofthe compression, so that data may be uncompressed on restore ifappropriate.

Data Analysis, Reporting, and Management Operations

Data analysis, reporting, and management operations can differ from datamovement operations in that they do not necessarily involve copying,migration or other transfer of data between different locations in thesystem. For instance, data analysis operations may involve processing(e.g., offline processing) or modification of already stored primarydata 112 and/or secondary copies 116. However, in some embodiments dataanalysis operations are performed in conjunction with data movementoperations. Some data analysis operations include content indexingoperations and classification operations which can be useful inleveraging data under management to enhance search and other features.

Classification Operations/Content Indexing

In some embodiments, information management system 100 analyzes andindexes characteristics, content, and metadata associated with primarydata 112 (“online content indexing”) and/or secondary copies 116(“off-line content indexing”). Content indexing can identify files orother data objects based on content (e.g., user-defined keywords orphrases, other keywords/phrases that are not defined by a user, etc.),and/or metadata (e.g., email metadata such as “to,” “from,” “cc,” “bcc,”attachment name, received time, etc.). Content indexes may be searchedand search results may be restored.

System 100 generally organizes and catalogues the results into a contentindex, which may be stored within media agent database 152, for example.The content index can also include the storage locations of or pointerreferences to indexed data in primary data 112 and/or secondary copies116. Results may also be stored elsewhere in system 100 (e.g., inprimary storage device 104 or in secondary storage device 108). Suchcontent index data provides storage manager 140 or other components withan efficient mechanism for locating primary data 112 and/or secondarycopies 116 of data objects that match particular criteria, thus greatlyincreasing the search speed capability of system 100. For instance,search criteria can be specified by a user through user interface 158 ofstorage manager 140. Moreover, when system 100 analyzes data and/ormetadata in secondary copies 116 to create an “off-line content index,”this operation has no significant impact on the performance of clientcomputing devices 102 and thus does not take a toll on the productionenvironment. Examples of content indexing techniques are provided inU.S. Pat. No. 8,170,995.

One or more components, such as a content index engine, can beconfigured to scan data and/or associated metadata for classificationpurposes to populate a database (or other data structure) ofinformation, which can be referred to as a “data classificationdatabase” or a “metabase.” Depending on the embodiment, the dataclassification database(s) can be organized in a variety of differentways, including centralization, logical sub-divisions, and/or physicalsub-divisions. For instance, one or more data classification databasesmay be associated with different subsystems or tiers within system 100.As an example, there may be a first metabase associated with primarystorage subsystem 117 and a second metabase associated with secondarystorage subsystem 118. In other cases, metabase(s) may be associatedwith individual components, e.g., client computing devices 102 and/ormedia agents 144. In some embodiments, a data classification databasemay reside as one or more data structures within management database146, may be otherwise associated with storage manager 140, and/or mayreside as a separate component. In some cases, metabase(s) may beincluded in separate database(s) and/or on separate storage device(s)from primary data 112 and/or secondary copies 116, such that operationsrelated to the metabase(s) do not significantly impact performance onother components of system 100. In other cases, metabase(s) may bestored along with primary data 112 and/or secondary copies 116. Files orother data objects can be associated with identifiers (e.g., tagentries, etc.) to facilitate searches of stored data objects. Among anumber of other benefits, the metabase can also allow efficient,automatic identification of files or other data objects to associatewith secondary copy or other information management operations. Forinstance, a metabase can dramatically improve the speed with whichsystem 100 can search through and identify data as compared to otherapproaches that involve scanning an entire file system. Examples ofmetabases and data classification operations are provided in U.S. Pat.Nos. 7,734,669 and 7,747,579.

Management and Reporting Operations

Certain embodiments leverage the integrated ubiquitous nature of system100 to provide useful system-wide management and reporting. Operationsmanagement can generally include monitoring and managing the health andperformance of system 100 by, without limitation, performing errortracking, generating granular storage/performance metrics (e.g., jobsuccess/failure information, deduplication efficiency, etc.), generatingstorage modeling and costing information, and the like. As an example,storage manager 140 or another component in system 100 may analyzetraffic patterns and suggest and/or automatically route data to minimizecongestion. In some embodiments, the system can generate predictionsrelating to storage operations or storage operation information. Suchpredictions, which may be based on a trending analysis, may predictvarious network operations or resource usage, such as network trafficlevels, storage media use, use of bandwidth of communication links, useof media agent components, etc. Further examples of traffic analysis,trend analysis, prediction generation, and the like are described inU.S. Pat. No. 7,343,453.

In some configurations having a hierarchy of storage operation cells, amaster storage manager 140 may track the status of subordinate cells,such as the status of jobs, system components, system resources, andother items, by communicating with storage managers 140 (or othercomponents) in the respective storage operation cells. Moreover, themaster storage manager 140 may also track status by receiving periodicstatus updates from the storage managers 140 (or other components) inthe respective cells regarding jobs, system components, systemresources, and other items. In some embodiments, a master storagemanager 140 may store status information and other information regardingits associated storage operation cells and other system information inits management database 146 and/or index 150 (or in another location).The master storage manager 140 or other component may also determinewhether certain storage-related or other criteria are satisfied, and mayperform an action or trigger event (e.g., data migration) in response tothe criteria being satisfied, such as where a storage threshold is metfor a particular volume, or where inadequate protection exists forcertain data. For instance, data from one or more storage operationcells is used to dynamically and automatically mitigate recognizedrisks, and/or to advise users of risks or suggest actions to mitigatethese risks. For example, an information management policy may specifycertain requirements (e.g., that a storage device should maintain acertain amount of free space, that secondary copies should occur at aparticular interval, that data should be aged and migrated to otherstorage after a particular period, that data on a secondary volumeshould always have a certain level of availability and be restorablewithin a given time period, that data on a secondary volume may bemirrored or otherwise migrated to a specified number of other volumes,etc.). If a risk condition or other criterion is triggered, the systemmay notify the user of these conditions and may suggest (orautomatically implement) a mitigation action to address the risk. Forexample, the system may indicate that data from a primary copy 112should be migrated to a secondary storage device 108 to free up space onprimary storage device 104. Examples of the use of risk factors andother triggering criteria are described in U.S. Pat. No. 7,343,453.

In some embodiments, system 100 may also determine whether a metric orother indication satisfies particular storage criteria sufficient toperform an action. For example, a storage policy or other definitionmight indicate that a storage manager 140 should initiate a particularaction if a storage metric or other indication drops below or otherwisefails to satisfy specified criteria such as a threshold of dataprotection. In some embodiments, risk factors may be quantified intocertain measurable service or risk levels. For example, certainapplications and associated data may be considered to be more importantrelative to other data and services. Financial compliance data, forexample, may be of greater importance than marketing materials, etc.Network administrators may assign priority values or “weights” tocertain data and/or applications corresponding to the relativeimportance. The level of compliance of secondary copy operationsspecified for these applications may also be assigned a certain value.Thus, the health, impact, and overall importance of a service may bedetermined, such as by measuring the compliance value and calculatingthe product of the priority value and the compliance value to determinethe “service level” and comparing it to certain operational thresholdsto determine whether it is acceptable. Further examples of the servicelevel determination are provided in U.S. Pat. No. 7,343,453.

System 100 may additionally calculate data costing and data availabilityassociated with information management operation cells. For instance,data received from a cell may be used in conjunction withhardware-related information and other information about system elementsto determine the cost of storage and/or the availability of particulardata. Exemplary information generated could include how fast aparticular department is using up available storage space, how long datawould take to recover over a particular pathway from a particularsecondary storage device, costs over time, etc. Moreover, in someembodiments, such information may be used to determine or predict theoverall cost associated with the storage of certain information. Thecost associated with hosting a certain application may be based, atleast in part, on the type of media on which the data resides, forexample. Storage devices may be assigned to a particular costcategories, for example. Further examples of costing techniques aredescribed in U.S. Pat. No. 7,343,453.

Any of the above types of information (e.g., information related totrending, predictions, job, cell or component status, risk, servicelevel, costing, etc.) can generally be provided to users via userinterface 158 in a single integrated view or console (not shown). Reporttypes may include: scheduling, event management, media management anddata aging. Available reports may also include backup history, dataaging history, auxiliary copy history, job history, library and drive,media in library, restore history, and storage policy, etc., withoutlimitation. Such reports may be specified and created at a certain pointin time as a system analysis, forecasting, or provisioning tool.Integrated reports may also be generated that illustrate storage andperformance metrics, risks and storage costing information. Moreover,users may create their own reports based on specific needs. Userinterface 158 can include an option to graphically depict the variouscomponents in the system using appropriate icons. As one example, userinterface 158 may provide a graphical depiction of primary storagedevices 104, secondary storage devices 108, data agents 142 and/or mediaagents 144, and their relationship to one another in system 100.

In general, the operations management functionality of system 100 canfacilitate planning and decision-making. For example, in someembodiments, a user may view the status of some or all jobs as well asthe status of each component of information management system 100. Usersmay then plan and make decisions based on this data. For instance, auser may view high-level information regarding secondary copy operationsfor system 100, such as job status, component status, resource status(e.g., communication pathways, etc.), and other information. The usermay also drill down or use other means to obtain more detailedinformation regarding a particular component, job, or the like. Furtherexamples are provided in U.S. Pat. No. 7,343,453.

System 100 can also be configured to perform system-wide e-discoveryoperations in some embodiments. In general, e-discovery operationsprovide a unified collection and search capability for data in thesystem, such as data stored in secondary storage devices 108 (e.g.,backups, archives, or other secondary copies 116). For example, system100 may construct and maintain a virtual repository for data stored insystem 100 that is integrated across source applications 110, differentstorage device types, etc. According to some embodiments, e-discoveryutilizes other techniques described herein, such as data classificationand/or content indexing.

Information Management Policies

An information management policy 148 can include a data structure orother information source that specifies a set of parameters (e.g.,criteria and rules) associated with secondary copy and/or otherinformation management operations.

One type of information management policy 148 is a “storage policy.”According to certain embodiments, a storage policy generally comprises adata structure or other information source that defines (or includesinformation sufficient to determine) a set of preferences or othercriteria for performing information management operations. Storagepolicies can include one or more of the following: (1) what data will beassociated with the storage policy, e.g., subclient; (2) a destinationto which the data will be stored; (3) datapath information specifyinghow the data will be communicated to the destination; (4) the type ofsecondary copy operation to be performed; and (5) retention informationspecifying how long the data will be retained at the destination (see,e.g., FIG. 1E). Data associated with a storage policy can be logicallyorganized into subclients, which may represent primary data 112 and/orsecondary copies 116. A subclient may represent static or dynamicassociations of portions of a data volume. Subclients may representmutually exclusive portions. Thus, in certain embodiments, a portion ofdata may be given a label and the association is stored as a staticentity in an index, database or other storage location. Subclients mayalso be used as an effective administrative scheme of organizing dataaccording to data type, department within the enterprise, storagepreferences, or the like. Depending on the configuration, subclients cancorrespond to files, folders, virtual machines, databases, etc. In oneexemplary scenario, an administrator may find it preferable to separatee-mail data from financial data using two different subclients.

A storage policy can define where data is stored by specifying a targetor destination storage device (or group of storage devices). Forinstance, where the secondary storage device 108 includes a group ofdisk libraries, the storage policy may specify a particular disk libraryfor storing the subclients associated with the policy. As anotherexample, where the secondary storage devices 108 include one or moretape libraries, the storage policy may specify a particular tape libraryfor storing the subclients associated with the storage policy, and mayalso specify a drive pool and a tape pool defining a group of tapedrives and a group of tapes, respectively, for use in storing thesubclient data. While information in the storage policy can bestatically assigned in some cases, some or all of the information in thestorage policy can also be dynamically determined based on criteria setforth in the storage policy. For instance, based on such criteria, aparticular destination storage device(s) or other parameter of thestorage policy may be determined based on characteristics associatedwith the data involved in a particular secondary copy operation, deviceavailability (e.g., availability of a secondary storage device 108 or amedia agent 144), network status and conditions (e.g., identifiedbottlenecks), user credentials, and the like.

Datapath information can also be included in the storage policy. Forinstance, the storage policy may specify network pathways and componentsto utilize when moving the data to the destination storage device(s). Insome embodiments, the storage policy specifies one or more media agents144 for conveying data associated with the storage policy between thesource and destination. A storage policy can also specify the type(s) ofassociated operations, such as backup, archive, snapshot, auxiliarycopy, or the like. Furthermore, retention parameters can specify howlong the resulting secondary copies 116 will be kept (e.g., a number ofdays, months, years, etc.), perhaps depending on organizational needsand/or compliance criteria.

When adding a new client computing device 102, administrators canmanually configure information management policies 148 and/or othersettings, e.g., via user interface 158. However, this can be an involvedprocess resulting in delays, and it may be desirable to begin dataprotection operations quickly, without awaiting human intervention.Thus, in some embodiments, system 100 automatically applies a defaultconfiguration to client computing device 102. As one example, when oneor more data agent(s) 142 are installed on a client computing device102, the installation script may register the client computing device102 with storage manager 140, which in turn applies the defaultconfiguration to the new client computing device 102. In this manner,data protection operations can begin substantially immediately. Thedefault configuration can include a default storage policy, for example,and can specify any appropriate information sufficient to begin dataprotection operations. This can include a type of data protectionoperation, scheduling information, a target secondary storage device108, data path information (e.g., a particular media agent 144), and thelike.

Another type of information management policy 148 is a “schedulingpolicy,” which specifies when and how often to perform operations.Scheduling parameters may specify with what frequency (e.g., hourly,weekly, daily, event-based, etc.) or under what triggering conditionssecondary copy or other information management operations are to takeplace. Scheduling policies in some cases are associated with particularcomponents, such as a subclient, client computing device 102, and thelike.

Another type of information management policy 148 is an “audit policy”(or “security policy”), which comprises preferences, rules and/orcriteria that protect sensitive data in system 100. For example, anaudit policy may define “sensitive objects” which are files or dataobjects that contain particular keywords (e.g., “confidential,” or“privileged”) and/or are associated with particular keywords (e.g., inmetadata) or particular flags (e.g., in metadata identifying a documentor email as personal, confidential, etc.). An audit policy may furtherspecify rules for handling sensitive objects. As an example, an auditpolicy may require that a reviewer approve the transfer of any sensitiveobjects to a cloud storage site, and that if approval is denied for aparticular sensitive object, the sensitive object should be transferredto a local primary storage device 104 instead. To facilitate thisapproval, the audit policy may further specify how a secondary storagecomputing device 106 or other system component should notify a reviewerthat a sensitive object is slated for transfer.

Another type of information management policy 148 is a “provisioningpolicy,” which can include preferences, priorities, rules, and/orcriteria that specify how client computing devices 102 (or groupsthereof) may utilize system resources, such as available storage oncloud storage and/or network bandwidth. A provisioning policy specifies,for example, data quotas for particular client computing devices 102(e.g., a number of gigabytes that can be stored monthly, quarterly orannually). Storage manager 140 or other components may enforce theprovisioning policy. For instance, media agents 144 may enforce thepolicy when transferring data to secondary storage devices 108. If aclient computing device 102 exceeds a quota, a budget for the clientcomputing device 102 (or associated department) may be adjustedaccordingly or an alert may trigger.

While the above types of information management policies 148 aredescribed as separate policies, one or more of these can be generallycombined into a single information management policy 148. For instance,a storage policy may also include or otherwise be associated with one ormore scheduling, audit, or provisioning policies or operationalparameters thereof. Moreover, while storage policies are typicallyassociated with moving and storing data, other policies may beassociated with other types of information management operations. Thefollowing is a non-exhaustive list of items that information managementpolicies 148 may specify:

-   -   schedules or other timing information, e.g., specifying when        and/or how often to perform information management operations;    -   the type of secondary copy 116 and/or copy format (e.g.,        snapshot, backup, archive, HSM, etc.);    -   a location or a class or quality of storage for storing        secondary copies 116 (e.g., one or more particular secondary        storage devices 108);    -   preferences regarding whether and how to encrypt, compress,        deduplicate, or otherwise modify or transform secondary copies        116;    -   which system components and/or network pathways (e.g., preferred        media agents 144) should be used to perform secondary storage        operations;    -   resource allocation among different computing devices or other        system components used in performing information management        operations (e.g., bandwidth allocation, available storage        capacity, etc.);    -   whether and how to synchronize or otherwise distribute files or        other data objects across multiple computing devices or hosted        services; and    -   retention information specifying the length of time primary data        112 and/or secondary copies 116 should be retained, e.g., in a        particular class or tier of storage devices, or within the        system 100.

Information management policies 148 can additionally specify or dependon historical or current criteria that may be used to determine whichrules to apply to a particular data object, system component, orinformation management operation, such as:

-   -   frequency with which primary data 112 or a secondary copy 116 of        a data object or metadata has been or is predicted to be used,        accessed, or modified;    -   time-related factors (e.g., aging information such as time since        the creation or modification of a data object);    -   deduplication information (e.g., hashes, data blocks,        deduplication block size, deduplication efficiency or other        metrics);    -   an estimated or historic usage or cost associated with different        components (e.g., with secondary storage devices 108);    -   the identity of users, applications 110, client computing        devices 102 and/or other computing devices that created,        accessed, modified, or otherwise utilized primary data 112 or        secondary copies 116;    -   a relative sensitivity (e.g., confidentiality, importance) of a        data object, e.g., as determined by its content and/or metadata;    -   the current or historical storage capacity of various storage        devices;    -   the current or historical network capacity of network pathways        connecting various components within the storage operation cell;    -   access control lists or other security information; and    -   the content of a particular data object (e.g., its textual        content) or of metadata associated with the data object.

Exemplary Storage Policy and Secondary Copy Operations

FIG. 1E includes a data flow diagram depicting performance of secondarycopy operations by an embodiment of information management system 100,according to an exemplary storage policy 148A. System 100 includes astorage manager 140, a client computing device 102 having a file systemdata agent 142A and an email data agent 142B operating thereon, aprimary storage device 104, two media agents 144A, 144B, and twosecondary storage devices 108: a disk library 108A and a tape library108B. As shown, primary storage device 104 includes primary data 112A,which is associated with a logical grouping of data associated with afile system (“file system subclient”), and primary data 112B, which is alogical grouping of data associated with email (“email subclient”). Thetechniques described with respect to FIG. 1E can be utilized inconjunction with data that is otherwise organized as well.

As indicated by the dashed box, the second media agent 144B and tapelibrary 108B are “off-site,” and may be remotely located from the othercomponents in system 100 (e.g., in a different city, office building,etc.). Indeed, “off-site” may refer to a magnetic tape located in remotestorage, which must be manually retrieved and loaded into a tape driveto be read. In this manner, information stored on the tape library 108Bmay provide protection in the event of a disaster or other failure atthe main site(s) where data is stored.

The file system subclient 112A in certain embodiments generallycomprises information generated by the file system and/or operatingsystem of client computing device 102, and can include, for example,file system data (e.g., regular files, file tables, mount points, etc.),operating system data (e.g., registries, event logs, etc.), and thelike. The e-mail subclient 112B can include data generated by an e-mailapplication operating on client computing device 102, e.g., mailboxinformation, folder information, emails, attachments, associateddatabase information, and the like. As described above, the subclientscan be logical containers, and the data included in the correspondingprimary data 112A and 112B may or may not be stored contiguously.

The exemplary storage policy 148A includes backup copy preferences orrule set 160, disaster recovery copy preferences or rule set 162, andcompliance copy preferences or rule set 164. Backup copy rule set 160specifies that it is associated with file system subclient 166 and emailsubclient 168. Each of subclients 166 and 168 are associated with theparticular client computing device 102. Backup copy rule set 160 furtherspecifies that the backup operation will be written to disk library 108Aand designates a particular media agent 144A to convey the data to disklibrary 108A. Finally, backup copy rule set 160 specifies that backupcopies created according to rule set 160 are scheduled to be generatedhourly and are to be retained for 30 days. In some other embodiments,scheduling information is not included in storage policy 148A and isinstead specified by a separate scheduling policy.

Disaster recovery copy rule set 162 is associated with the same twosubclients 166 and 168. However, disaster recovery copy rule set 162 isassociated with tape library 108B, unlike backup copy rule set 160.Moreover, disaster recovery copy rule set 162 specifies that a differentmedia agent, namely 144B, will convey data to tape library 108B.Disaster recovery copies created according to rule set 162 will beretained for 60 days and will be generated daily. Disaster recoverycopies generated according to disaster recovery copy rule set 162 canprovide protection in the event of a disaster or other catastrophic dataloss that would affect the backup copy 116A maintained on disk library108A.

Compliance copy rule set 164 is only associated with the email subclient168, and not the file system subclient 166. Compliance copies generatedaccording to compliance copy rule set 164 will therefore not includeprimary data 112A from the file system subclient 166. For instance, theorganization may be under an obligation to store and maintain copies ofemail data for a particular period of time (e.g., 10 years) to complywith state or federal regulations, while similar regulations do notapply to file system data. Compliance copy rule set 164 is associatedwith the same tape library 108B and media agent 144B as disasterrecovery copy rule set 162, although a different storage device or mediaagent could be used in other embodiments. Finally, compliance copy ruleset 164 specifies that the copies it governs will be generated quarterlyand retained for 10 years.

Secondary Copy Jobs

A logical grouping of secondary copy operations governed by a rule setand being initiated at a point in time may be referred to as a“secondary copy job” (and sometimes may be called a “backup job,” eventhough it is not necessarily limited to creating only backup copies).Secondary copy jobs may be initiated on demand as well. Steps 1-9 belowillustrate three secondary copy jobs based on storage policy 148A.

Referring to FIG. 1E, at step 1, storage manager 140 initiates a backupjob according to the backup copy rule set 160, which logically comprisesall the secondary copy operations necessary to effectuate rules 160 instorage policy 148A every hour, including steps 1-4 occurring hourly.For instance, a scheduling service running on storage manager 140accesses backup copy rule set 160 or a separate scheduling policyassociated with client computing device 102 and initiates a backup jobon an hourly basis. Thus, at the scheduled time, storage manager 140sends instructions to client computing device 102 (i.e., to both dataagent 142A and data agent 142B) to begin the backup job.

At step 2, file system data agent 142A and email data agent 142B onclient computing device 102 respond to instructions from storage manager140 by accessing and processing the respective subclient primary data112A and 112B involved in the backup copy operation, which can be foundin primary storage device 104. Because the secondary copy operation is abackup copy operation, the data agent(s) 142A, 142B may format the datainto a backup format or otherwise process the data suitable for a backupcopy.

At step 3, client computing device 102 communicates the processed filesystem data (e.g., using file system data agent 142A) and the processedemail data (e.g., using email data agent 142B) to the first media agent144A according to backup copy rule set 160, as directed by storagemanager 140. Storage manager 140 may further keep a record in managementdatabase 146 of the association between media agent 144A and one or moreof: client computing device 102, file system subclient 112A, file systemdata agent 142A, email subclient 112B, email data agent 142B, and/orbackup copy 116A.

The target media agent 144A receives the data-agent-processed data fromclient computing device 102, and at step 4 generates and conveys backupcopy 116A to disk library 108A to be stored as backup copy 116A, againat the direction of storage manager 140 and according to backup copyrule set 160. Media agent 144A can also update its index 153 to includedata and/or metadata related to backup copy 116A, such as informationindicating where the backup copy 116A resides on disk library 108A,where the email copy resides, where the file system copy resides, dataand metadata for cache retrieval, etc. Storage manager 140 may similarlyupdate its index 150 to include information relating to the secondarycopy operation, such as information relating to the type of operation, aphysical location associated with one or more copies created by theoperation, the time the operation was performed, status informationrelating to the operation, the components involved in the operation, andthe like. In some cases, storage manager 140 may update its index 150 toinclude some or all of the information stored in index 153 of mediaagent 144A. At this point, the backup job may be considered complete.After the 30-day retention period expires, storage manager 140 instructsmedia agent 144A to delete backup copy 116A from disk library 108A andindexes 150 and/or 153 are updated accordingly.

At step 5, storage manager 140 initiates another backup job for adisaster recovery copy according to the disaster recovery rule set 162.This includes steps 5-7 occurring daily for creating disaster recoverycopy 116B. By way of illustrating the scalable aspects and off-loadingprinciples embedded in system 100, disaster recovery copy 116B is basedon backup copy 116A and not on primary data 112A and 112B.

At step 6, based on instructions received from storage manager 140 atstep 5, the specified media agent 144B retrieves the most recent backupcopy 116A from disk library 108A.

At step 7, again at the direction of storage manager 140 and asspecified in disaster recovery copy rule set 162, media agent 144B usesthe retrieved data to create a disaster recovery copy 1168 and store itto tape library 1088. In some cases, disaster recovery copy 1168 is adirect, mirror copy of backup copy 116A, and remains in the backupformat. In other embodiments, disaster recovery copy 1168 may be furthercompressed or encrypted, or may be generated in some other manner, suchas by using primary data 112A and 1128 from primary storage device 104as sources. The disaster recovery copy operation is initiated once a dayand disaster recovery copies 1168 are deleted after 60 days; indexes 153and/or 150 are updated accordingly when/after each informationmanagement operation is executed and/or completed. The present backupjob may be considered completed.

At step 8, storage manager 140 initiates another backup job according tocompliance rule set 164, which performs steps 8-9 quarterly to createcompliance copy 116C. For instance, storage manager 140 instructs mediaagent 144B to create compliance copy 116C on tape library 1088, asspecified in the compliance copy rule set 164.

At step 9 in the example, compliance copy 116C is generated usingdisaster recovery copy 1168 as the source. This is efficient, becausedisaster recovery copy resides on the same secondary storage device andthus no network resources are required to move the data. In otherembodiments, compliance copy 116C is instead generated using primarydata 1128 corresponding to the email subclient or using backup copy 116Afrom disk library 108A as source data. As specified in the illustratedexample, compliance copies 116C are created quarterly, and are deletedafter ten years, and indexes 153 and/or 150 are kept up-to-dateaccordingly.

Exemplary Applications of Storage Policies—Information GovernancePolicies and Classification

Again referring to FIG. 1E, storage manager 140 may permit a user tospecify aspects of storage policy 148A. For example, the storage policycan be modified to include information governance policies to define howdata should be managed in order to comply with a certain regulation orbusiness objective. The various policies may be stored, for example, inmanagement database 146. An information governance policy may align withone or more compliance tasks that are imposed by regulations or businessrequirements. Examples of information governance policies might includea Sarbanes-Oxley policy, a HIPAA policy, an electronic discovery(e-discovery) policy, and so on.

Information governance policies allow administrators to obtain differentperspectives on an organization's online and offline data, without theneed for a dedicated data silo created solely for each differentviewpoint. As described previously, the data storage systems hereinbuild an index that reflects the contents of a distributed data set thatspans numerous clients and storage devices, including both primary dataand secondary copies, and online and offline copies. An organization mayapply multiple information governance policies in a top-down manner overthat unified data set and indexing schema in order to view andmanipulate the data set through different lenses, each of which isadapted to a particular compliance or business goal. Thus, for example,by applying an e-discovery policy and a Sarbanes-Oxley policy, twodifferent groups of users in an organization can conduct two verydifferent analyses of the same underlying physical set of data/copies,which may be distributed throughout the information management system.

An information governance policy may comprise a classification policy,which defines a taxonomy of classification terms or tags relevant to acompliance task and/or business objective. A classification policy mayalso associate a defined tag with a classification rule. Aclassification rule defines a particular combination of criteria, suchas users who have created, accessed or modified a document or dataobject; file or application types; content or metadata keywords; clientsor storage locations; dates of data creation and/or access; reviewstatus or other status within a workflow (e.g., reviewed orun-reviewed); modification times or types of modifications; and/or anyother data attributes in any combination, without limitation. Aclassification rule may also be defined using other classification tagsin the taxonomy. The various criteria used to define a classificationrule may be combined in any suitable fashion, for example, via Booleanoperators, to define a complex classification rule. As an example, ane-discovery classification policy might define a classification tag“privileged” that is associated with documents or data objects that (1)were created or modified by legal department staff, or (2) were sent toor received from outside counsel via email, or (3) contain one of thefollowing keywords: “privileged” or “attorney” or “counsel,” or otherlike terms. Accordingly, all these documents or data objects will beclassified as “privileged.”

One specific type of classification tag, which may be added to an indexat the time of indexing, is an “entity tag.” An entity tag may be, forexample, any content that matches a defined data mask format. Examplesof entity tags might include, e.g., social security numbers (e.g., anynumerical content matching the formatting mask XXX-XX-XXXX), credit cardnumbers (e.g., content having a 13-16 digit string of numbers), SKUnumbers, product numbers, etc. A user may define a classification policyby indicating criteria, parameters or descriptors of the policy via agraphical user interface, such as a form or page with fields to befilled in, pull-down menus or entries allowing one or more of severaloptions to be selected, buttons, sliders, hypertext links or other knownuser interface tools for receiving user input, etc. For example, a usermay define certain entity tags, such as a particular product number orproject ID. In some implementations, the classification policy can beimplemented using cloud-based techniques. For example, the storagedevices may be cloud storage devices, and the storage manager 140 mayexecute cloud service provider API over a network to classify datastored on cloud storage devices.

Restore Operations from Secondary Copies

While not shown in FIG. 1E, at some later point in time, a restoreoperation can be initiated involving one or more of secondary copies116A, 1168, and 116C. A restore operation logically takes a selectedsecondary copy 116, reverses the effects of the secondary copy operationthat created it, and stores the restored data to primary storage where aclient computing device 102 may properly access it as primary data. Amedia agent 144 and an appropriate data agent 142 (e.g., executing onthe client computing device 102) perform the tasks needed to complete arestore operation. For example, data that was encrypted, compressed,and/or deduplicated in the creation of secondary copy 116 will becorrespondingly rehydrated (reversing deduplication), uncompressed, andunencrypted into a format appropriate to primary data. Metadata storedwithin or associated with the secondary copy 116 may be used during therestore operation. In general, restored data should be indistinguishablefrom other primary data 112. Preferably, the restored data has fullyregained the native format that may make it immediately usable byapplication 110.

As one example, a user may manually initiate a restore of backup copy116A, e.g., by interacting with user interface 158 of storage manager140 or with a web-based console with access to system 100. Storagemanager 140 may accesses data in its index 150 and/or managementdatabase 146 (and/or the respective storage policy 148A) associated withthe selected backup copy 116A to identify the appropriate media agent144A and/or secondary storage device 108A where the secondary copyresides. The user may be presented with a representation (e.g., stub,thumbnail, listing, etc.) and metadata about the selected secondarycopy, in order to determine whether this is the appropriate copy to berestored, e.g., date that the original primary data was created. Storagemanager 140 will then instruct media agent 144A and an appropriate dataagent 142 on the target client computing device 102 to restore secondarycopy 116A to primary storage device 104. A media agent may be selectedfor use in the restore operation based on a load balancing algorithm, anavailability based algorithm, or other criteria. The selected mediaagent, e.g., 144A, retrieves secondary copy 116A from disk library 108A.For instance, media agent 144A may access its index 153 to identify alocation of backup copy 116A on disk library 108A, or may accesslocation information residing on disk library 108A itself.

In some cases a backup copy 116A that was recently created or accessed,may be cached to speed up the restore operation. In such a case, mediaagent 144A accesses a cached version of backup copy 116A residing inindex 153, without having to access disk library 108A for some or all ofthe data. Once it has retrieved backup copy 116A, the media agent 144Acommunicates the data to the requesting client computing device 102.Upon receipt, file system data agent 142A and email data agent 142B mayunpack (e.g., restore from a backup format to the native applicationformat) the data in backup copy 116A and restore the unpackaged data toprimary storage device 104. In general, secondary copies 116 may berestored to the same volume or folder in primary storage device 104 fromwhich the secondary copy was derived; to another storage location orclient computing device 102; to shared storage, etc. In some cases, thedata may be restored so that it may be used by an application 110 of adifferent version/vintage from the application that created the originalprimary data 112.

Exemplary Secondary Copy Formatting

The formatting and structure of secondary copies 116 can vary dependingon the embodiment. In some cases, secondary copies 116 are formatted asa series of logical data units or “chunks” (e.g., 512 MB, 1 GB, 2 GB, 4GB, or 8 GB chunks). This can facilitate efficient communication andwriting to secondary storage devices 108, e.g., according to resourceavailability. For example, a single secondary copy 116 may be written ona chunk-by-chunk basis to one or more secondary storage devices 108. Insome cases, users can select different chunk sizes, e.g., to improvethroughput to tape storage devices. Generally, each chunk can include aheader and a payload. The payload can include files (or other dataunits) or subsets thereof included in the chunk, whereas the chunkheader generally includes metadata relating to the chunk, some or all ofwhich may be derived from the payload. For example, during a secondarycopy operation, media agent 144, storage manager 140, or other componentmay divide files into chunks and generate headers for each chunk byprocessing the files. Headers can include a variety of information suchas file and/or volume identifier(s), offset(s), and/or other informationassociated with the payload data items, a chunk sequence number, etc.Importantly, in addition to being stored with secondary copy 116 onsecondary storage device 108, chunk headers can also be stored to index153 of the associated media agent(s) 144 and/or to index 150 associatedwith storage manager 140. This can be useful for providing fasterprocessing of secondary copies 116 during browsing, restores, or otheroperations. In some cases, once a chunk is successfully transferred to asecondary storage device 108, the secondary storage device 108 returnsan indication of receipt, e.g., to media agent 144 and/or storagemanager 140, which may update their respective indexes 153, 150accordingly. During restore, chunks may be processed (e.g., by mediaagent 144) according to the information in the chunk header toreassemble the files.

Data can also be communicated within system 100 in data channels thatconnect client computing devices 102 to secondary storage devices 108.These data channels can be referred to as “data streams,” and multipledata streams can be employed to parallelize an information managementoperation, improving data transfer rate, among other advantages. Exampledata formatting techniques including techniques involving datastreaming, chunking, and the use of other data structures in creatingsecondary copies are described in U.S. Pat. Nos. 7,315,923, 8,156,086,and 8,578,120.

FIGS. 1F and 1G are diagrams of example data streams 170 and 171,respectively, which may be employed for performing informationmanagement operations. Referring to FIG. 1F, data agent 142 forms datastream 170 from source data associated with a client computing device102 (e.g., primary data 112). Data stream 170 is composed of multiplepairs of stream header 172 and stream data (or stream payload) 174. Datastreams 170 and 171 shown in the illustrated example are for asingle-instanced storage operation, and a stream payload 174 thereforemay include both single-instance (SI) data and/or non-SI data. A streamheader 172 includes metadata about the stream payload 174. This metadatamay include, for example, a length of the stream payload 174, anindication of whether the stream payload 174 is encrypted, an indicationof whether the stream payload 174 is compressed, an archive fileidentifier (ID), an indication of whether the stream payload 174 issingle instanceable, and an indication of whether the stream payload 174is a start of a block of data.

Referring to FIG. 1G, data stream 171 has the stream header 172 andstream payload 174 aligned into multiple data blocks. In this example,the data blocks are of size 64 KB. The first two stream header 172 andstream payload 174 pairs comprise a first data block of size 64 KB. Thefirst stream header 172 indicates that the length of the succeedingstream payload 174 is 63 KB and that it is the start of a data block.The next stream header 172 indicates that the succeeding stream payload174 has a length of 1 KB and that it is not the start of a new datablock. Immediately following stream payload 174 is a pair comprising anidentifier header 176 and identifier data 178. The identifier header 176includes an indication that the succeeding identifier data 178 includesthe identifier for the immediately previous data block. The identifierdata 178 includes the identifier that the data agent 142 generated forthe data block. The data stream 171 also includes other stream header172 and stream payload 174 pairs, which may be for SI data and/or non-SIdata.

FIG. 1H is a diagram illustrating data structures 180 that may be usedto store blocks of SI data and non-SI data on a storage device (e.g.,secondary storage device 108). According to certain embodiments, datastructures 180 do not form part of a native file system of the storagedevice. Data structures 180 include one or more volume folders 182, oneor more chunk folders 184/185 within the volume folder 182, and multiplefiles within chunk folder 184. Each chunk folder 184/185 includes ametadata file 186/187, a metadata index file 188/189, one or morecontainer files 190/191/193, and a container index file 192/194.Metadata file 186/187 stores non-SI data blocks as well as links to SIdata blocks stored in container files. Metadata index file 188/189stores an index to the data in the metadata file 186/187. Containerfiles 190/191/193 store SI data blocks. Container index file 192/194stores an index to container files 190/191/193. Among other things,container index file 192/194 stores an indication of whether acorresponding block in a container file 190/191/193 is referred to by alink in a metadata file 186/187. For example, data block B2 in thecontainer file 190 is referred to by a link in metadata file 187 inchunk folder 185. Accordingly, the corresponding index entry incontainer index file 192 indicates that data block B2 in container file190 is referred to. As another example, data block B1 in container file191 is referred to by a link in metadata file 187, and so thecorresponding index entry in container index file 192 indicates thatthis data block is referred to.

As an example, data structures 180 illustrated in FIG. 1H may have beencreated as a result of separate secondary copy operations involving twoclient computing devices 102. For example, a first secondary copyoperation on a first client computing device 102 could result in thecreation of the first chunk folder 184, and a second secondary copyoperation on a second client computing device 102 could result in thecreation of the second chunk folder 185. Container files 190/191 in thefirst chunk folder 184 would contain the blocks of SI data of the firstclient computing device 102. If the two client computing devices 102have substantially similar data, the second secondary copy operation onthe data of the second client computing device 102 would result in mediaagent 144 storing primarily links to the data blocks of the first clientcomputing device 102 that are already stored in the container files190/191. Accordingly, while a first secondary copy operation may resultin storing nearly all of the data subject to the operation, subsequentsecondary storage operations involving similar data may result insubstantial data storage space savings, because links to already storeddata blocks can be stored instead of additional instances of datablocks.

If the operating system of the secondary storage computing device 106 onwhich media agent 144 operates supports sparse files, then when mediaagent 144 creates container files 190/191/193, it can create them assparse files. A sparse file is a type of file that may include emptyspace (e.g., a sparse file may have real data within it, such as at thebeginning of the file and/or at the end of the file, but may also haveempty space in it that is not storing actual data, such as a contiguousrange of bytes all having a value of zero). Having container files190/191/193 be sparse files allows media agent 144 to free up space incontainer files 190/191/193 when blocks of data in container files190/191/193 no longer need to be stored on the storage devices. In someexamples, media agent 144 creates a new container file 190/191/193 whena container file 190/191/193 either includes 100 blocks of data or whenthe size of the container file 190 exceeds 50 MB. In other examples,media agent 144 creates a new container file 190/191/193 when acontainer file 190/191/193 satisfies other criteria (e.g., it containsfrom approx. 100 to approx. 1000 blocks or when its size exceedsapproximately 50 MB to 1 GB). In some cases, a file on which a secondarycopy operation is performed may comprise a large number of data blocks.For example, a 100 MB file may comprise 400 data blocks of size 256 KB.If such a file is to be stored, its data blocks may span more than onecontainer file, or even more than one chunk folder. As another example,a database file of 20 GB may comprise over 40,000 data blocks of size512 KB. If such a database file is to be stored, its data blocks willlikely span multiple container files, multiple chunk folders, andpotentially multiple volume folders. Restoring such files may requireaccessing multiple container files, chunk folders, and/or volume foldersto obtain the requisite data blocks.

Using Backup Data for Replication and Disaster Recovery (“LiveSynchronization”)

There is an increased demand to off-load resource intensive informationmanagement tasks (e.g., data replication tasks) away from productiondevices (e.g., physical or virtual client computing devices) in order tomaximize production efficiency. At the same time, enterprises expectaccess to readily-available up-to-date recovery copies in the event offailure, with little or no production downtime.

FIG. 2A illustrates a system 200 configured to address these and otherissues by using backup or other secondary copy data to synchronize asource subsystem 201 (e.g., a production site) with a destinationsubsystem 203 (e.g., a failover site). Such a technique can be referredto as “live synchronization” and/or “live synchronization replication.”In the illustrated embodiment, the source client computing devices 202 ainclude one or more virtual machines (or “VMs”) executing on one or morecorresponding VM host computers 205 a, though the source need not bevirtualized. The destination site 203 may be at a location that isremote from the production site 201, or may be located in the same datacenter, without limitation. One or more of the production site 201 anddestination site 203 may reside at data centers at known geographiclocations, or alternatively may operate “in the cloud.”

The synchronization can be achieved by generally applying an ongoingstream of incremental backups from the source subsystem 201 to thedestination subsystem 203, such as according to what can be referred toas an “incremental forever” approach. FIG. 2A illustrates an embodimentof a data flow which may be orchestrated at the direction of one or morestorage managers (not shown). At step 1, the source data agent(s) 242 aand source media agent(s) 244 a work together to write backup or othersecondary copies of the primary data generated by the source clientcomputing devices 202 a into the source secondary storage device(s) 208a. At step 2, the backup/secondary copies are retrieved by the sourcemedia agent(s) 244 a from secondary storage. At step 3, source mediaagent(s) 244 a communicate the backup/secondary copies across a networkto the destination media agent(s) 244 b in destination subsystem 203.

As shown, the data can be copied from source to destination in anincremental fashion, such that only changed blocks are transmitted, andin some cases multiple incremental backups are consolidated at thesource so that only the most current changed blocks are transmitted toand applied at the destination. An example of live synchronization ofvirtual machines using the “incremental forever” approach is found inU.S. Patent Application No. 62/265,339 entitled “Live Synchronizationand Management of Virtual Machines across Computing and VirtualizationPlatforms and Using Live Synchronization to Support Disaster Recovery.”Moreover, a deduplicated copy can be employed to further reduce networktraffic from source to destination. For instance, the system can utilizethe deduplicated copy techniques described in U.S. Pat. No. 9,239,687,entitled “Systems and Methods for Retaining and Using Data BlockSignatures in Data Protection Operations.”

At step 4, destination media agent(s) 244 b write the receivedbackup/secondary copy data to the destination secondary storagedevice(s) 208 b. At step 5, the synchronization is completed when thedestination media agent(s) and destination data agent(s) 242 b restorethe backup/secondary copy data to the destination client computingdevice(s) 202 b. The destination client computing device(s) 202 b may bekept “warm” awaiting activation in case failure is detected at thesource. This synchronization/replication process can incorporate thetechniques described in U.S. patent application Ser. No. 14/721,971,entitled “Replication Using Deduplicated Secondary Copy Data.”

Where the incremental backups are applied on a frequent, on-going basis,the synchronized copies can be viewed as mirror or replication copies.Moreover, by applying the incremental backups to the destination site203 using backup or other secondary copy data, the production site 201is not burdened with the synchronization operations. Because thedestination site 203 can be maintained in a synchronized “warm” state,the downtime for switching over from the production site 201 to thedestination site 203 is substantially less than with a typical restorefrom secondary storage. Thus, the production site 201 may flexibly andefficiently fail over, with minimal downtime and with relativelyup-to-date data, to a destination site 203, such as a cloud-basedfailover site. The destination site 203 can later be reversesynchronized back to the production site 201, such as after repairs havebeen implemented or after the failure has passed.

Integrating with the Cloud Using File System Protocols

Given the ubiquity of cloud computing, it can be increasingly useful toprovide data protection and other information management services in ascalable, transparent, and highly plug-able fashion. FIG. 2B illustratesan information management system 200 having an architecture thatprovides such advantages, and incorporates use of a standard file systemprotocol between primary and secondary storage subsystems 217, 218. Asshown, the use of the network file system (NFS) protocol (or any anotherappropriate file system protocol such as that of the Common InternetFile System (CIFS)) allows data agent 242 to be moved from the primarystorage subsystem 217 to the secondary storage subsystem 218. Forinstance, as indicated by the dashed box 206 around data agent 242 andmedia agent 244, data agent 242 can co-reside with media agent 244 onthe same server (e.g., a secondary storage computing device such ascomponent 106), or in some other location in secondary storage subsystem218.

Where NFS is used, for example, secondary storage subsystem 218allocates an NFS network path to the client computing device 202 or toone or more target applications 210 running on client computing device202. During a backup or other secondary copy operation, the clientcomputing device 202 mounts the designated NFS path and writes data tothat NFS path. The NFS path may be obtained from NFS path data 215stored locally at the client computing device 202, and which may be acopy of or otherwise derived from NFS path data 219 stored in thesecondary storage subsystem 218.

Write requests issued by client computing device(s) 202 are received bydata agent 242 in secondary storage subsystem 218, which translates therequests and works in conjunction with media agent 244 to process andwrite data to a secondary storage device(s) 208, thereby creating abackup or other secondary copy. Storage manager 240 can include apseudo-client manager 217, which coordinates the process by, among otherthings, communicating information relating to client computing device202 and application 210 (e.g., application type, client computing deviceidentifier, etc.) to data agent 242, obtaining appropriate NFS path datafrom the data agent 242 (e.g., NFS path information), and deliveringsuch data to client computing device 202.

Conversely, during a restore or recovery operation client computingdevice 202 reads from the designated NFS network path, and the readrequest is translated by data agent 242. The data agent 242 then workswith media agent 244 to retrieve, re-process (e.g., re-hydrate,decompress, decrypt), and forward the requested data to client computingdevice 202 using NFS.

By moving specialized software associated with system 200 such as dataagent 242 off the client computing devices 202, the architectureeffectively decouples the client computing devices 202 from theinstalled components of system 200, improving both scalability andplug-ability of system 200. Indeed, the secondary storage subsystem 218in such environments can be treated simply as a read/write NFS targetfor primary storage subsystem 217, without the need for informationmanagement software to be installed on client computing devices 202. Asone example, an enterprise implementing a cloud production computingenvironment can add VM client computing devices 202 without installingand configuring specialized information management software on theseVMs. Rather, backups and restores are achieved transparently, where thenew VMs simply write to and read from the designated NFS path. Anexample of integrating with the cloud using file system protocols orso-called “infinite backup” using NFS share is found in U.S. PatentApplication No. 62/294,920, entitled “Data Protection Operations Basedon Network Path Information.” Examples of improved data restorationscenarios based on network-path information, including using storedbackups effectively as primary data sources, may be found in U.S. PatentApplication No. 62/297,057, entitled “Data Restoration Operations Basedon Network Path Information.”

Highly Scalable Managed Data Pool Architecture

Enterprises are seeing explosive data growth in recent years, often fromvarious applications running in geographically distributed locations.FIG. 2C shows a block diagram of an example of a highly scalable,managed data pool architecture useful in accommodating such data growth.The illustrated system 200, which may be referred to as a “web-scale”architecture according to certain embodiments, can be readilyincorporated into both open compute/storage and common-cloudarchitectures.

The illustrated system 200 includes a grid 245 of media agents 244logically organized into a control tier 231 and a secondary or storagetier 233. Media agents assigned to the storage tier 233 can beconfigured to manage a secondary storage pool 208 as a deduplicationstore, and be configured to receive client write and read requests fromthe primary storage subsystem 217, and direct those requests to thesecondary tier 233 for servicing. For instance, media agents CMA1-CMA3in the control tier 231 maintain and consult one or more deduplicationdatabases 247, which can include deduplication information (e.g., datablock hashes, data block links, file containers for deduplicated files,etc.) sufficient to read deduplicated files from secondary storage pool208 and write deduplicated files to secondary storage pool 208. Forinstance, system 200 can incorporate any of the deduplication systemsand methods shown and described in U.S. Pat. No. 9,020,900, entitled“Distributed Deduplicated Storage System,” and U.S. Pat. Pub. No.2014/0201170, entitled “High Availability Distributed DeduplicatedStorage System.”

Media agents SMA1-SMA6 assigned to the secondary tier 233 receive writeand read requests from media agents CMA1-CMA3 in control tier 231, andaccess secondary storage pool 208 to service those requests. Mediaagents CMA1-CMA3 in control tier 231 can also communicate with secondarystorage pool 208, and may execute read and write requests themselves(e.g., in response to requests from other control media agentsCMA1-CMA3) in addition to issuing requests to media agents in secondarytier 233. Moreover, while shown as separate from the secondary storagepool 208, deduplication database(s) 247 can in some cases reside instorage devices in secondary storage pool 208.

As shown, each of the media agents 244 (e.g., CMA1-CMA3, SMA1-SMA6,etc.) in grid 245 can be allocated a corresponding dedicated partition251A-2511, respectively, in secondary storage pool 208. Each partition251 can include a first portion 253 containing data associated with(e.g., stored by) media agent 244 corresponding to the respectivepartition 251. System 200 can also implement a desired level ofreplication, thereby providing redundancy in the event of a failure of amedia agent 244 in grid 245. Along these lines, each partition 251 canfurther include a second portion 255 storing one or more replicationcopies of the data associated with one or more other media agents 244 inthe grid.

System 200 can also be configured to allow for seamless addition ofmedia agents 244 to grid 245 via automatic configuration. As oneexample, a storage manager (not shown) or other appropriate componentmay determine that it is appropriate to add an additional node tocontrol tier 231, and perform some or all of the following: (i) assessthe capabilities of a newly added or otherwise available computingdevice as satisfying a minimum criteria to be configured as or hosting amedia agent in control tier 231; (ii) confirm that a sufficient amountof the appropriate type of storage exists to support an additional nodein control tier 231 (e.g., enough disk drive capacity exists in storagepool 208 to support an additional deduplication database 247); (iii)install appropriate media agent software on the computing device andconfigure the computing device according to a pre-determined template;(iv) establish a partition 251 in the storage pool 208 dedicated to thenewly established media agent 244; and (v) build any appropriate datastructures (e.g., an instance of deduplication database 247). An exampleof highly scalable managed data pool architecture or so-called web-scalearchitecture for storage and data management is found in U.S. PatentApplication No. 62/273,286 entitled “Redundant and Robust DistributedDeduplication Data Storage System.”

The embodiments and components thereof disclosed in FIGS. 2A, 2B, and2C, as well as those in FIGS. 1A-1H, may be implemented in anycombination and permutation to satisfy data storage management andinformation management needs at one or more locations and/or datacenters.

Performance Metric Miss Prediction

As described above, being able to detect a situation in which theinformation management system 100 is unable to satisfy a performancemetric can reduce the likelihood that data loss or poor secondary copyoperation performance occurs. As one example, a performance metric candefine a number of secondary copy operation jobs that are to becompleted within a threshold period of time. A secondary copy operationjob can include a backup job, a restore job, a snapshot job, an archivejob, and/or the like. A secondary copy operation job can further be afull secondary copy operation job, an incremental secondary copyoperation job, a differential secondary copy operation job, and/or thelike. For ease of explanation, the functionality of the informationmanagement system 100 is described herein with respect to theperformance metric defining a number of secondary copy operation jobsthat are to be completed within a threshold period of time. This is notmeant to be limiting, however. For example, the same or similarfunctionality can be implemented by the information management system100 to predict other types of performance metric misses, such as thoseassociated with metrics that define a maximum number of secondary copyoperation jobs that can fail within a threshold period of time, amaximum length of time for a secondary copy operation job to becompleted, a maximum number of secondary copy operation jobs that can bepending during a threshold period of time, and/or the like.

FIG. 3 is a block diagram illustrating some salient portions of a system300 for predicting performance metric misses, according to anembodiment. As illustrated in FIG. 3, the storage manager 140 caninclude various components to implement the performance metric missfunctionality. For example, the storage manager 140 can include ananomaly detector 342, a job length predictor 344, a job failureclassifier 346, a performance metric (PM) predictor 348, and the storagemanager database 146. The anomaly detector 342 can detect possibleanomalies in one or more secondary copy operation jobs being performedby one or more of the secondary storage computing devices 106, the joblength predictor 344 can predict the length of time to complete one ormore secondary copy operation jobs being performed by one or more of thesecondary storage computing devices 106, the job failure classifier 346can predict whether any of the one or more secondary copy operation jobsbeing performed one or more of the secondary storage computing devices106 is likely to fail, and the PM predictor 348 can use some or all ofthe information determined by the anomaly detector 342, the job lengthpredictor 344, and/or the job classifier 346 to determine whether aperformance metric miss may occur.

The storage manager database 146 can store data related to the executionof historical, current, and/or future secondary copy operation jobs. Forexample, the data can include neural network training data used to traina neural network to predict whether a secondary copy operation job willsucceed or fail, machine learning training data used to train a machinelearning model to predict a length of time that it may take for asecondary copy operation job to complete, neural network input dataassociated with various secondary copy operation jobs that can beapplied to the trained neural network as an input to determine whetherthe respective secondary copy operation jobs will succeed or fail, andmachine learning input data associated with various secondary copyoperation jobs that can be applied to the trained machine learning modelas an input to determine a length of time it may take to complete therespective secondary copy operation jobs. The storage manager 140 canobtain the neural network training data, the machine learning trainingdata, the neural network input data, and/or the machine learning inputdata from one or more of the client computing devices 110 and/or thesecondary storage computing devices 106, and store such data in thestorage manager database 146. Alternatively or in addition, the storagemanager 140 can obtain the neural network training data, the machinelearning training data, the neural network input data, and/or themachine learning input data locally from hardware components of thestorage manager 140.

As an example, the neural network training data and/or the neuralnetwork input data can include data for one or more secondary copyoperation jobs. For each secondary copy operation job, the neuralnetwork training data and/or neural network input data can include: ajob ID; a status of the job (e.g., pending, canceled, failed, completed,etc.); an ID of the client computing device 110 associated with primarydata on which the respective secondary copy operation job is or has beenperformed; an ID of the secondary storage computing device 106 thatwill, is, or has performed the respective secondary copy operation job;a bkpLevel; a type of application from which primary data on which therespective secondary copy operation job will, is, or has been performedoriginates; a time it took to complete the respective secondary copyoperation job, a number of secondary copy operation job streams runningsimultaneously on a secondary storage computing device 106 thatperformed, is performing, or will be performing the respective secondarycopy operation job; a number of secondary copy operation job streamsthat are requested on a secondary storage computing device 106 thatperformed, is performing, or will be performing the respective secondarycopy operation job; data throughput of a secondary storage computingdevice 106 that will, is, or has performed the respective secondary copyoperation job; a number of times a secondary storage computing device106 has attempted to perform the respective secondary copy operationjob; a scantype; a priority assigned to the respective secondary copyoperation job; any failures in scanning for primary data on which therespective secondary copy operation job was, is, or will be performed;any failures in scanning for folders that include primary data on whichthe respective secondary copy operation job was, is, or will beperformed; any failures in scanning for secondary copy data on which therespective secondary copy operation job was, is, or will be performed;any failures in scanning for folders that include secondary copy data onwhich the respective secondary copy operation job was, is, or will beperformed; CPU usage of the storage manager 140 before, after, and/orduring performance of the respective secondary copy operation job;physical memory usage of the storage manager 140 before, after, and/orduring performance of the respective secondary copy operation job; thepercentage of the physical memory of the storage manager 140 that isfree before, after, and/or during performance of the respectivesecondary copy operation job; virtual memory usage of the storagemanager 140 before, after, and/or during performance of the respectivesecondary copy operation job; the percentage of the virtual memory ofthe storage manager 140 that is free before, after, and/or duringperformance of the respective secondary copy operation job; anindication of one or more events that have, will, or are occurring inrelation to the storage manager 140 and performance of the respectivesecondary copy operation job; a status of a mutex associated with thestorage manager 140 before, during, and/or after performance of therespective secondary copy operation job; a status of processes runningon the storage manager 140 before, during, and/or after performance ofthe respective secondary copy operation job; a status of a semaphoreassociated with the storage manager 140 before, during, and/or afterperformance of the respective secondary copy operation job; a number ofthreads running on the storage manager 140 before, during, and/or afterperformance of the respective secondary copy operation job; a number ofsecondary copy operation jobs that are running before, during, and/orafter performance of the respective secondary copy operation job; anumber of secondary copy operation jobs that are pending before, during,and/or after performance of the respective secondary copy operation job;a number of secondary copy operation jobs that are waiting to beexecuted before, during, and/or after performance of the respectivesecondary copy operation job; an amount of data that is beingtransferred between components of the information management system 300before, during, and/or after performance of the respective secondarycopy operation job; a number of streams being used to transfer databetween components of the information management system 300 before,during, and/or after performance of the respective secondary copyoperation job; a number of streams in the information management system300 that are waiting to perform actions before, during, and/or afterperformance of the respective secondary copy operation job; a jobactivity level (e.g., how many jobs are active) before, during, and/orafter performance of the respective secondary copy operation job; amaximum number of job restarts associated with the respective secondarycopy operation job; a number of long running secondary copy operationjobs present in the information management system 300 before, during,and/or after performance of the respective secondary copy operation job;a secondary copy operation job success rate for secondary copy operationjobs executed by the secondary storage computing device 106 that will,is, or has executed the respective secondary copy operation job; anumber of secondary copy operation jobs that failed and were executed bythe secondary storage computing device 106 that will, is, or hasexecuted the secondary copy operation job; a parallelAdmins; anindication of or a number of secondary copy operation jobs thatpreviously failed on the secondary storage computing device 106 thatwill, is, or has executed the respective secondary copy operation joband/or on the client computing device 110 from which primary dataassociated with the respective secondary copy operation job originates;an ID of a component in the information management system 300 that hasfailed or is failing in relation to performance of the secondary copyoperation job; a message number of an error generated for the componentin the information management system 300 that has failed or is failingin relation to performance of the secondary copy operation job; an alertcolor level of an alert generated in response to the failure of thecomponent in the information management system 300 in relation toperformance of the secondary copy operation job that may be displayed ina user interface; a number of secondary storage computing devices 106 inthe information management system 300 available to perform secondarycopy operation jobs, such as the respective secondary copy operationjob; CPU usage of one or more of the secondary storage computing devices106 before, after, and/or during performance of the respective secondarycopy operation job, including CPU usage of the secondary storagecomputing device 106 performing the respective secondary copy operationjob; physical memory usage of one or more of the secondary storagecomputing devices 106 before, after, and/or during performance of therespective secondary copy operation job, including CPU usage of thesecondary storage computing device 106 performing the respectivesecondary copy operation job; the percentage of the physical memory ofone or more of the secondary storage computing devices 106 that is freebefore, after, and/or during performance of the respective secondarycopy operation job, including the percentage of the physical memory ofthe secondary storage computing device 106 performing the respectivesecondary copy operation job; virtual memory usage of one or more of thesecondary storage computing devices 106 before, after, and/or duringperformance of the respective secondary copy operation job, includingthe virtual memory usage of the secondary storage computing device 106performing the respective secondary copy operation job; the percentageof the virtual memory of one or more of the secondary storage computingdevices 106 that is free before, after, and/or during performance of therespective secondary copy operation job, including the percentage of thevirtual memory of the secondary storage computing device 106 performingthe respective secondary copy operation job; an indication of one ormore events that have, will, or are occurring in relation to one or moreof the secondary storage computing devices 106 and performance of therespective secondary copy operation job, including an indication of oneor more events associated with the secondary storage computing device106 performing the respective secondary copy operation job; a status ofa mutex associated with one or more of the secondary storage computingdevices 106 before, during, and/or after performance of the respectivesecondary copy operation job, including a status of a mutex associatedwith the secondary storage computing device 106 performing therespective secondary copy operation job; a status of processes runningon one or more of the secondary storage computing devices 106 before,during, and/or after performance of the respective secondary copyoperation job, including a status of processes running on the secondarystorage computing device 106 performing the respective secondary copyoperation job; a status of a semaphore associated with one or more ofthe secondary storage computing devices 106 before, during, and/or afterperformance of the respective secondary copy operation job, including astatus of a semaphore associated with the secondary storage computingdevice 106 performing the secondary copy operation job; a number ofthreads running on one or more of the secondary storage computingdevices 106 before, during, and/or after performance of the respectivesecondary copy operation job, including a number of threads running onthe secondary storage computing device 106 performing the respectivesecondary copy operation job; and a number of parallel streams runningon a storage manager 140 and/or one or more of the secondary storagecomputing devices 106 before, during, and/or after performance of therespective secondary copy operation job, including a number of parallelstreams running on the secondary storage computing device 106 performingthe respective secondary copy operation job.

As another example, the machine learning training data and/or themachine learning input data can include data for one or more secondarycopy operation jobs. For each secondary copy operation job, the machinelearning training data and/or the machine learning input data caninclude: a time taken to perform the respective secondary copy operationjob (e.g., a time at which the respective secondary copy operation jobbegan, a time at which the respective secondary copy operation jobfinished, a duration of time between when the respective secondary copyoperation job began and finished, etc.), a number of secondary copyoperation jobs performed by the secondary storage computing device 106that is, has, or will be performing the respective secondary copyoperation job that failed during a threshold period of time (which maybe indicated by error codes included in the data that identify acomponent of the information management system 300 that caused theerror); a number of consecutive secondary copy operation jobs performedby the secondary storage computing device 106 that is, has, or will beperforming the respective secondary copy operation job that failed; thetype of secondary copy operation job that is the respective secondarycopy operation job; the size of the primary data backed up, snapped,archived, etc. in conjunction with execution of the respective secondarycopy operation job; the size of the secondary copy data restored inconjunction with execution of the respective secondary copy operationjob; a number of times the secondary storage computing device 106attempted execution of the respective secondary copy operation job untilthe job successfully completed; the number of streams running on thesecondary storage computing device 106 that is, has, or will beperforming the respective secondary copy operation (with the number ofstreams being close to the maximum number of streams that the secondarystorage computing device 106 can handle indicating that services of thesecondary storage computing device 106 may be in bad shape); and anumber of secondary copy operation jobs being executed by the secondarystorage computing device 106 that is, has, or will be performing therespective secondary copy operation (with the number of secondary copyoperation jobs being close to the maximum number of secondary copyoperation jobs that the secondary storage computing device 106 canhandle indicating that services of the secondary storage computingdevice 106 may be in bad shape).

The anomaly detector 342 can detect anomalies in the execution ofsecondary copy operation jobs by various secondary storage computingdevices 106. For example, the anomaly detector 342 can detect possibleanomalies in response to an instruction from the PM predictor 348 toprovide anomaly information. In particular, jobs data corresponding toone or more secondary copy operation jobs initiated during a thresholdtime period by a particular secondary storage computing device 106 maybe stored in the storage manager database 146 in the form of a set oftime-series jobs data (e.g., where the x-axis represents a time or timeperiod during which secondary copy operation jobs are initiated and they-axis represents one of a number of secondary copy operation jobs thatsucceeded, a number of secondary copy operation jobs that failed, anumber of secondary copy operation jobs that were killed or aborted, anumber of secondary copy operation jobs that were suspended, or a numberof secondary copy operation jobs that are pending during the time ortime period). As described herein, the jobs data may have a seasonalpattern (e.g., have a consistent number of succeeded, failed, killed,suspended, an/or pending jobs during particular time periods, such asduring a certain hour, during a certain day of the week, during acertain week of the month, etc.) and/or a trend pattern (e.g., have anumber of succeeded, failed, killed, suspended, an/or pending jobs thatrise at a certain rate over a period of time, have a number ofsucceeded, failed, killed, suspended, an/or pending jobs that fall at acertain rate over a period of time, etc.). As an illustrative example, anumber of secondary copy operation jobs performed by a first secondarystorage computing device 106 that succeeded may occur more often onMondays than on other days of the week (e.g., the seasonal pattern), andmay increase by 10 jobs every week (e.g., the trend pattern). An anomalytherefore may be a situation in which a number of succeeded, failed,killed, suspended, and/or pending jobs performed by a secondary storagecomputing device 106 does not comport with the seasonal pattern or thetrend pattern associated with the secondary storage computing device 106or the information management system 300 in general.

The anomaly detector 342 can retrieve, from the storage manager database146, some or all of the jobs data corresponding to one or more secondarycopy operation jobs initiated during a threshold time period by aparticular secondary storage computing device 106. The anomaly detector342 can then perform a time-series decomposition on the retrievedtime-series jobs data to separate the time-series jobs data into aseasonal component, a trend component, and an error component (alsoreferred to herein as a residual component). The seasonal component maybe the portion of the time-series jobs data that represents the seasonalpattern of the secondary copy operation job(s), the trend component maybe the portion of the time-series jobs data that represents the trendpattern of the secondary copy operation job(s), and the error componentmay be the portion of the time-series jobs data that represents theremaining jobs data for the secondary copy operation job(s).

In some embodiments, the secondary copy operation job(s) may correspondto multiple seasonal patterns. For example, the number of succeeded,failed, killed, suspended, an/or pending jobs may spike or drop duringparticular times of a day, during particular days of the week, andduring particular weeks of a month. In such cases, the anomaly detector342 can perform multiple decompositions—one for each seasonal patternsuch that in each decomposition, the time-series jobs data is decomposedinto a seasonal component corresponding to one of the seasonal patterns,a trend component, and an error component—or perform a singledecomposition in which the time-series jobs data is decomposed intomultiple seasonal components, a trend component, and an error component.

In an embodiment, the anomaly detector 342 can use the locally estimatedscatterplot smoothing (LOESS) process to decompose the time-series jobsdata. Alternatively, the anomaly detector 342 can use the Dickey Fullertest, periodogram (e.g., Fast Fourier Transform), seasonal trenddecomposition, the generalized extreme studentized deviate (GESD) test,and/or the like to decompose the time-series jobs data. Although thesemathematical techniques may be known, their application to the presentproblems at hand, the sequencing of operations to find anomalies, andthe combination of parameters analyzed within the particulararchitectures of the illustrative embodiments represent technologicalimprovements over conventional systems.

As an illustrative example, the anomaly detector 342 can use STL(Seasonal and Trend decomposition using Loess) to decompose atime-series into its components as follows:

-   -   1. Initialize trend as T(0)v=0 and R(0)v    -   2. Outer loop—Calculate robustness weights. Run n(o) times        -   Calculate Rv        -   Calculate robustness weights ρv=B(|Rv|/h) where            h=6*median(|Rv|) B is the bi-square weight function [1]        -   On initial loop, ρv=1    -   3. Inner loop—Iteratively calculate trend and seasonal terms.        Run n(i) times        -   Detrend: Yv−Tv(k) where k is the loop number. If the            observed value Yv is missing, then the detrended term is            also missing.        -   Cycle-subseries smoothing: The detrended time series is            broken into cyclesubseries. For example, monthly data with a            periodicity of twelve months would yield twelve            cycle-subseries, one of which would be all of the months of            January. Each cycle-subseries is then loess smoothed with            q=n(s) and d=1. The smoothed values yield a temporary            seasonal time series Ck+1.        -   Low-pass filter: The low pass filter on Ck+1 yields Lk+1.            This filter is the application of two moving averages of lag            equal to three followed by loess filtering with q=n(I) and            d=1. n(I) is defaulted the smallest odd integer greater than            the period (e.g. 13 for monthly data). The output of the            low-pass filter is Lk+1        -   Detrending of smoothed cycle-subseries: Sk+1=Ck+1−Lk+1. This            is the k+1-th estimate of seasonal component. Importantly,            the low-pass filter causes this seasonal time series to            average to be nearly zero.        -   Deseasonalizing: Y−Sk+1        -   Trend smoothing: Loess smooth the deseasonalized time series            with q=n(t). Results in Tk+1, the k+1-th estimate of the            trend component.    -   4. After obtaining univariate series, run GESD test to find        whether a given value is an outlier or not.

Once the anomaly detector 342 has decomposed the time-series jobs data,the anomaly detector 342 can determine a variance in association withthe error component(s). For example, the error component(s) mayrepresent the number of occurrences of succeeded, failed, killed,suspended, an/or pending jobs during various times or time periods afterremoving from the count the occurrences that are attributable to theseasonal pattern(s) and/or the trend pattern. The anomaly detector 342can apply a Box and Whisker analysis to the error component(s) todetermine a positive occurrence threshold value (e.g., an upper extremein a Box and Whisker plot that is above the median, upper quartile, andupper whisker) that, if exceeded, indicates a possible occurrenceanomaly (e.g., the succeeded, failed, killed, suspended, and/or pendingjobs are occurring too often) and/or a negative occurrence thresholdvalue (e.g., a lower extreme in a Box and Whisker plot that is below themedian, lower quartile, and lower whisker) that, if not exceeded,indicates a possible occurrence anomaly (e.g., the succeeded, failed,killed, suspended, and/or pending jobs are not occurring often enough).As an illustrative example, the positive occurrence threshold value maybe N (e.g., 1, 2, 3, 4, 5, etc.) times the mean, median, standarddeviation, variance, etc. above the mean or median, and the negativeoccurrence threshold value may be N (e.g., 1, 2, 3, 4, 5, etc.) timesthe mean, median, standard deviation, variance, etc. below the mean ormedian. Thus, if the number of the succeeded, failed, killed, suspended,and/or pending jobs represented by the error component(s) during aparticular time or time period exceeds the positive occurrence thresholdvalue or does not exceed the negative occurrence threshold value, thenthe anomaly detector 342 may identify this time or time period as beinga time or time period during which an anomaly may have occurred. Theanomaly detector 342 can provide this information to the PM predictor348.

Similarly, the anomaly detector 342 can apply the Box and Whiskeranalysis to the error component(s) to identify an anomaly in which theduration of time between succeeded, failed, killed, suspended, and/orpending jobs is too long or too short. For example, applying the Box andWhisker analysis to the error component(s) may result in creation of aminimum duration threshold and/or a maximum duration threshold, wherethe minimum duration threshold is measured between occurrences ofsucceeded, failed, killed, suspended, and/or pending jobs and, if notexceeded, indicates that the succeeded, failed, killed, suspended,and/or pending jobs are occurring too often, and where the maximumduration threshold is measured between occurrences of succeeded, failed,killed, suspended, and/or pending jobs and, if exceeded, indicates thatthe succeeded, failed, killed, suspended, and/or pending jobs are notoccurring often enough. The anomaly detector 342 can determine whetheroccurrences of succeeded, failed, killed, suspended, and/or pending jobsare occurring too often or not often enough, and provide thisinformation to the PM predictor 348.

The job length predictor 344 can be configured to predict a length oftime to perform or execute a subject secondary copy operation job. Asdescribed herein, the machine learning training data stored in thestorage manager database 146 can include the types of secondary copyoperation jobs previously executed by a secondary storage computingdevice 106 that is or will be executing the subject secondary copyoperation job, the size of the primary data backed up, snapped,archived, etc. in conjunction with one or more of the previouslyexecuted secondary copy operation jobs, the size of the secondary copydata restored in conjunction with one or more of the previously executedsecondary copy operation jobs, a time that one or more of the previoussecondary copy operation jobs was started, a time that one or more ofthe previous secondary copy operation jobs finished, a number of timesthe secondary storage computing device 106 attempted execution of one ormore of the previous secondary copy operation jobs until the jobsuccessfully completed, data related to the operation of the secondarystorage computing device 106 that performed one or more of the previoussecondary copy operation jobs (e.g., the status of the computingresources of the secondary storage computing device 106), and/or any ofthe other types of data described herein.

The job length predictor 344 can retrieve, from the storage managerdatabase 146, the machine learning training data corresponding to thestorage manager 140 and/or the secondary storage computing device 106that is or will be performing the secondary copy operation job. The joblength predictor 344 can then train a machine learning model using theretrieved machine learning training data. The trained machine learningmodel may output a prediction of a time to complete a secondary copyoperation job when provided as input(s) an indication of a secondarycopy operation job to complete and machine learning input dataassociated with the secondary copy operation job to complete. The joblength predictor 344 can train the machine learning model prior toreceiving a request (e.g., from the PM predictor 348) to provide aprediction of a length of time to perform a secondary copy operation joband/or after receiving form the PM predictor 348 a request to provide aprediction of a length of time to perform a secondary copy operationjob.

After the machine learning model is trained, the job length predictor344 can provide an indication of a predicted time to execute or performa particular secondary copy operation job in response to a request fromthe PM predictor 348. For example, the PM predictor 348 can request apredicted time to perform or execute a first secondary copy operationjob, such as a secondary copy operation job that is currently running ona secondary storage computing device 106 or that is scheduled to run ona secondary storage computing device 106 at some future time. Inresponse, the job length predictor 344 can retrieve machine learninginput data corresponding to the first secondary copy operation job, suchas a number of secondary copy operation jobs performed by the secondarystorage computing device 106 that is or will be performing the firstsecondary copy operation job that failed during a threshold period oftime, a number of consecutive secondary copy operation jobs performed bythe secondary storage computing device 106 that is or will be performingthe first secondary copy operation job that failed, a time at whichexecution of the secondary copy operation job started or is expected tostart, the type of secondary copy operation job that is the firstsecondary copy operation job, the size of the primary data that is orwill be backed up, snapped, archived, etc. in conjunction with executionof the first secondary copy operation job, the size of the secondarycopy data that is or will be restored in conjunction with execution ofthe first secondary copy operation job, data related to the operation ofthe secondary storage computing device 106 that will be or is performingthe first secondary copy operation job (e.g., the status of thecomputing resources of the secondary storage computing device 106),and/or any of the other types of data described herein.

The job length predictor 344 can then apply the machine learning inputdata as an input to the trained machine learning model, which causes thetrained machine learning model to output a prediction of a time it maytake to complete the first secondary copy operation job. The job lengthpredictor 344 can provide the prediction to the PM predictor 348.

The job failure classifier 346 can be configured to predict whether asecondary copy operation job is likely to succeed or fail. For example,neural network training data may be stored in the storage managerdatabase 146. The job failure classifier 346 can retrieve the neuralnetwork training data and train a neural network to predict whether asecondary copy operation job is likely to succeed or fail. The jobfailure classifier 346 can train the neural network using the neuralnetwork training data. The job failure classifier 346 can train theneural network prior to receiving a request from the PM predictor 348 toprovide a prediction on whether a particular secondary copy operationjob is likely to succeed or fail and/or after receiving the request fromthe PM predictor 348.

After the neural network is trained, the job failure classifier 346 canprovide neural network input data as an input to the trained neuralnetwork to obtain a prediction. For example, the job failure classifier346 can retrieve neural network input data from the storage managerdatabase 146 that corresponds with a particular secondary copy operationjob and/or a secondary storage computing device 106 that is or will beperforming the secondary copy operation job. The job failure classifier346 can then apply the retrieved neural network input data as an inputto the trained neural network, and provide the output (e.g., anindication of whether execution of a particular secondary copy operationjob is likely to succeed or fail) produced by the trained neural networkto the PM predictor 348.

The PM predictor 348 can be configured to predict whether a performancemetric associated with an information management system 300 is likely tobe satisfied. For example, the PM predictor 348 can access the storagemanager database 146 to determine whether any secondary copy operationjobs are scheduled to be performed within a threshold time period, havebeen performed during the threshold time period, and/or are currentlybeing performed during a time that falls within the threshold timeperiod. If no secondary copy operation jobs have been performed, arebeing performed, or are scheduled to be performed during the thresholdtime period, then the PM predictor 348 may generate a notification thata performance metric miss may occur, optionally with an explanation ofwhy the performance metric miss may occur (e.g., a reason that caused aninsufficient number of secondary copy operation jobs to complete priorto expiration of the threshold time period). The PM predictor 348 cantransmit the notification to a client computing device 110 and/or toanother user device operated by an administrator, can generate a userinterface that displays the notification and that can be rendered by aclient computing device 110 and/or user device, and/or the like.

If one or more secondary copy operation jobs have been performed, arebeing performed, and/or scheduled to be performed during the thresholdtime period, the PM predictor 348 may analyze the secondary copyoperation job(s) that are being performed and/or that are scheduled tobe performed to determine whether a threshold number of secondary copyoperation jobs will be completed before the threshold time periodexpires, where the threshold number of secondary copy operation jobs maybe defined by the performance metric of the information managementsystem 300. For example, for each secondary copy operation job that isbeing performed or that is scheduled to be performed during thethreshold time period, the PM predictor 348 can request the anomalydetector 342 to provide an indication of whether an anomaly is detectedwith respect to the secondary storage computing device 106 that isperforming or that is scheduled to perform the respective secondary copyoperation job, the PM predictor 348 can request the job length predictor344 to provide an indication of a predicted length of time it may taketo complete the respective secondary copy operation job, and/or the PMpredictor 348 can request the job failure classifier 346 to provide anindication of whether the respective secondary copy operation job islikely to succeed or fail. The PM predictor 348 can request thisinformation sequentially, simultaneously, and/or overlapping in time inany order.

The PM predictor 348 may use the information provided by the anomalydetector 342, the job length predictor 344, and/or the job failureclassifier 346 in determining whether a particular secondary copyoperation job is likely to be completed before the threshold time periodexpires. For example, the PM predictor 348 can use the informationprovided by the anomaly detector 342 to determine whether any anomalousactivity is taking place in association with the secondary storagecomputing device 106 that is performing or that is scheduled to performthe secondary copy operation job. If an anomaly is present—which mayindicate a history of failed secondary copy operation jobs or thatservices of the secondary storage computing device 106 are in badshape—the PM predictor 348 may determine that the secondary copyoperation job may not complete prior to expiration of the threshold timeperiod.

Alternatively or in addition, the PM predictor 348 can use theinformation provided by the job length predictor 344 to determine thelength of time it may take to execute the secondary copy operation job.The PM predictor 348 can then determine whether an operation windowbegins before the secondary copy operation job is predicted to finish.For example, the storage manager database 146 can store informationidentifying start and/or end times of various operation windows, such asblackout windows, maintenance windows, and/or the like. If an operationwindow may begin before the secondary copy operation job is predicted tofinish, the PM predictor 348 can determine the amount of timeoverlapping between the operation window and performance of thesecondary copy operation job, and add such time to the end of theoperation window to determine a time that the secondary copy operationjob may actually finish. The PM predictor 348 can then compare thisdetermined time to the threshold time period to determine whether thesecondary copy operation job is likely to finish before the thresholdtime period expires.

Alternatively or in addition, the PM predictor 348 can use theinformation provided by the job failure classifier 346 to determinewhether execution of the secondary copy operation job is likely tosucceed or fail. The PM predictor 348 may determine that the secondarycopy operation job may not complete prior to expiration of the thresholdtime period, even if the job length predictor 344 indicates that thesecondary copy operation job will finish before the threshold timeperiod expires, if the job failure classifier 346 predicts thatexecution of the secondary copy operation job is likely to fail.

Alternatively or in addition, the PM predictor 348 may consider otherdata in making the determination. For example, for a secondary copyoperation job that is currently running, the PM predictor 348 candetermine the data throughput of the secondary storage computing device106 that is performing the secondary copy operation job (e.g., based oninformation included in the storage manager database 146 and/or bycommunicating with the secondary storage computing device 106). If thedata throughput of the secondary storage computing device 106 is lessthan a threshold bitrate, then the PM predictor 348 may determine thatthe time to complete a secondary copy operation job may be an additionalamount based on the actual data throughput bitrate. The PM predictor 348can then determine whether the secondary copy operation job willcomplete before the threshold time period expires using this updatedcompletion time. As another example, for a secondary copy operation jobthat is currently running, the PM predictor 348 can determine, using thedata stored in the storage manager database 146 and/or by contacting thesecondary storage computing device 106 executing the secondary copyoperation job, whether the secondary copy operation job has been pendingwithout any progress for a threshold period of time. If the secondarycopy operation job has been pending for longer than the threshold periodof time, the PM predictor 348 may determine, alone or in conjunctionwith some or all of the other determinations described herein, that thesecondary copy operation job may not finish before the threshold timeperiod expires.

If the PM predictor 348 determines that enough secondary copy operationswill finish prior to the threshold time period expiring such that theperformance metric (as obtained from the storage manager database 146)will be satisfied, then the PM predictor 348 may not generate anynotification, even if the PM predictor 348 ultimately determines thatsome secondary copy operation jobs may take longer than usual to finishor are likely to fail. On the other hand, if the PM predictor 348determines that not enough secondary copy operations will finish priorto the threshold time period expiring such that the performance metricwill not be satisfied, then the PM predictor 348 may generate anotification.

As described above, the notification can be transmitted to a clientcomputing device 110 or a user device. The notification can also appearin a user interface generated for a client computing device 110 or auser device. For example, a user interface can display the notificationon a page dedicated to administration of the information managementsystem 300. If a user selects or hovers over the notification, the userinterface may detail additional information, such as an explanation ofwhy the PM predictor 348 has determined that the performance metric maynot be satisfied. The PM predictor 348 can derive the explanation frominformation obtained from the storage manager database 146 and/or fromthe anomaly detector 342, the job length predictor 344, and/or the jobfailure classifier 346. As an illustrative example, if the job lengthpredictor 344 predicts that a secondary copy operation job may takelonger than usual to complete due in part to the services of thesecondary storage computing device 106 that is to perform the secondarycopy operation job being in bad shape, then the job length predictor 344may provide one or more error codes to the PM predictor 348 thatultimately led the job length predictor 344 to predict that thesecondary copy operation job may take longer than usual to complete. Ifthe prediction by the job length predictor 344 is at least partly whythe PM predictor 348 determines that a performance metric miss mayoccur, then the notification may include one or more of the error codes.An example user interface that includes a notification is described ingreater detail below with respect to FIG. 10.

The PM predictor 348 may generate and provide the notification withsufficient notice before the threshold time period expires such that anadministrator may be able to resolve any potential issues so that theperformance metric can still be satisfied. As an illustrative example,if the job length predictor 344 predicts that a secondary copy operationjob will take 4 hours to complete and the PM predictor 348 determinesthat the secondary copy operation job will likely finish an hour beforethe threshold time period expires and a performance metric miss willoccur if the secondary copy operation job does not finish before thethreshold time period expires, then the PM predictor 348 may generatethe notification at least 5 hours before the threshold time periodexpires so that an administrator can optionally resolve an issue thatmay allow the secondary copy operation job to complete sooner.

While the present disclosure describes the storage manager 140 astraining a separate machine learning model and a separate neuralnetwork, this is not meant to be limiting. For example, the storagemanager 140 can combine some or all of the neural network training dataand the machine learning training data to train a single type ofartificial intelligence model, such as a neural network or a machinelearning model, that can either predict a length of time it may take toperform a secondary copy operation job, predict whether a secondary copyoperation job is likely to succeed or fail, or predict both data points.

In addition, while the present disclosure describes the storage manager140 as training a neural network to predict whether a secondary copyoperation job is likely to succeed or fail, this is not meant to belimiting. For example, the storage manager 140 can train another type ofartificial intelligence model, such as a machine learning model, usingthe neural network training data to predict whether a secondary copyoperation job is likely to succeed or fail. Similarly, while the presentdisclosure describes the storage manager 140 as training a machinelearning model to predict a time it may take to perform or execute asecondary copy operation job, this is not meant to be limiting. Forexample, the storage manager 140 can train another type of artificialintelligence model, such as a neural network, using the machine learningtraining data to predict a time it may take to perform or execute asecondary copy operation job.

While individual information management systems 300 may be customized tomeet a user's needs, data gathered from the individual informationmanagement systems 300 may be useful in identifying a possible solutionto a problem experienced by one information management system 300. Forexample, data gathered from individual information management systems300 can be used to train an artificial intelligence model (e.g., amachine learning model, a neural network, etc.) to predict a possiblesolution to a problem experienced by one information management system300. In fact, the artificial intelligence model can be periodicallyupdated or re-trained as new data is gathered from individualinformation management systems 300. In further embodiments, theinformation management system 300 experiencing the problem can beauto-tuned based on a possible solution predicted by the trainedartificial intelligence model, thereby reducing errors that may occur inmanually identifying and correcting potential problems.

FIG. 4 is a block diagram illustrating some salient portions of anenvironment for auto-tuning information management systems 300,according to an embodiment. As illustrated in FIG. 4, one or moreinformation management systems 300 may be in communication with acloud-based performance metric failure resolver 400 via a network 410.The cloud-based performance metric failure resolver 400 may use datagathered from the information management system(s) 300 to train anartificial intelligence model to predict possible solutions to problemsencountered by the information management system(s) 300 and/or mayauto-tune the information management system(s) 300 experiencingproblems.

The network 410 may include any wired network, wireless network, orcombination thereof. For example, the network 410 may be a personal areanetwork, local area network, wide area network, over-the-air broadcastnetwork (e.g., for radio or television), cable network, satellitenetwork, cellular telephone network, or combination thereof. As afurther example, the network 410 may be a publicly accessible network oflinked networks, possibly operated by various distinct parties, such asthe Internet. In some embodiments, the network 410 may be a private orsemi-private network, such as a corporate or university intranet. Thenetwork 410 may include one or more wireless networks, such as a GlobalSystem for Mobile Communications (GSM) network, a Code Division MultipleAccess (CDMA) network, a Long Term Evolution (LTE) network, or any othertype of wireless network. The network 104 can use protocols andcomponents for communicating via the Internet or any of the otheraforementioned types of networks. For example, the protocols used by thenetwork 410 may include Hypertext Transfer Protocol (HTTP), HTTP Secure(HTTPS), Message Queue Telemetry Transport (MQTT), ConstrainedApplication Protocol (CoAP), and the like. Protocols and components forcommunicating via the Internet or any of the other aforementioned typesof communication networks are well known to those skilled in the artand, thus, are not described in more detail herein.

The cloud-based performance metric failure resolver 400 can be acomputing system having memory storing computer-executable instructionsand one or more hardware processors in communication with the memory,where the computer-executable instructions, when executed by the one ormore hardware processors, cause the hardware processor(s) to train anartificial intelligence model to predict at least one solution to aproblem experienced by an information management system 300 (such as aperformance metric miss), to use the trained artificial intelligencemodel to determine a possible resolution to an information managementsystem 300 experiencing a problem like a performance metric miss, and/orto auto-tune the information management system 300 such that the problemis resolved (e.g., a performance metric is satisfied). The cloud-basedperformance metric failure resolver 400 may be a single computingdevice, or it may include multiple distinct computing devices, such ascomputer servers, logically or physically grouped together tocollectively operate as a server system. The components of thecloud-based performance metric failure resolver 400 can each beimplemented in application-specific hardware (e.g., a server computingdevice with one or more ASICs) such that no software is necessary, or asa combination of hardware and software. In addition, the modules andcomponents of the cloud-based performance metric failure resolver 400can be combined on one server computing device or separated individuallyor into groups on several server computing devices. In some embodiments,the cloud-based performance metric failure resolver 400 may includeadditional or fewer components than illustrated in FIG. 4.

The cloud-based performance metric failure resolver 400 may includevarious components to perform the functionality described herein. Forexample, the cloud-based performance metric failure resolver 400 mayinclude a machine learning (ML) training system 402, a PM behaviorpredictor 404, and a cell metrics data store 406.

The cloud-based performance metric failure resolver 400 may periodicallyrequest and/or one or more information management systems 300 mayperiodically transmit metrics related to secondary copy operation jobsexecuted therein and/or system operations (e.g., statuses of systemresources of components included therein). For example, the metrics mayinclude some or all of the neural network training data described above,some or all of the machine learning training data described above,and/or an indication of whether such data is associated with aperformance metric miss or a situation in which a performance metric wassatisfied. Once received, the cloud-based performance metric failureresolver 400 can store the metrics in the cell metrics data store 406.

Periodically and/or in response to receiving metrics from one or moreinformation management systems 300, the ML training system 402 canretrieve the newly received metrics and/or some or all of the metricsstored in the cell metrics data store 406 from the cell metrics datastore 406. The ML training system 402 can then train a machine learningmodel, a neural network, and/or any other type of artificialintelligence model using the retrieved metrics. In particular, themetrics may indicate the status of secondary copy operation jobs and/orsystem resources when a performance metric miss occurred and/or thestatus of secondary copy operation jobs and/or system resources when aperformance metric was satisfied. The ML training system 402 may processthe metrics in a way such that an artificial intelligence model istrained to predict an action that can be performed (or an action thatshould not be performed) to resolve a performance metric miss and/or anaction that can be performed (or an action that should not be performed)to avoid a performance metric miss. The ML training system 402 canupdate and/or re-train the trained artificial intelligence model, suchas when new metrics are received from an information management system300 or periodically.

At some later time, an information management system 300 may inform thecloud-based performance metric failure resolver 400 that a performancemetric miss occurred. As part of the notification, the informationmanagement system 300 may provide relevant metrics related to the statusof secondary copy operation jobs and/or system operations (where dataincluded in the notification may be collectively referred to herein as“PM miss data”). The PM behavior predictor 404 can retrieve the trainedartificial intelligence model from the ML training system 402 and canapply the received data as an input to the trained artificialintelligence model. As a result, the trained artificial intelligencemodel may output a possible resolution to a performance metric miss,such as an action that can be taken or should not be taken by theinformation management system 300 (e.g., shorten or reschedule anoperation window, schedule additional secondary copy operation jobs,replace a hardware component in the information management system 300,pause long running secondary copy operation jobs to free systemresources, add new hardware components in the information managementsystem 300 to increase system resources, etc.). The PM behaviorpredictor 404 can either provide the possible resolution to theinformation management system 300, such as to a device operated by anadministrator of the information management system 300. Alternatively orin addition, the PM behavior predictor 404 can generate an instructionand transmit the instruction to the information management system 300,where reception of the instruction causes the information managementsystem 300 (e.g., a storage manager 140 of the information managementsystem 300) to perform or not perform an action defined by theinstruction that may resolve the performance metric miss.

FIG. 5A illustrates a block diagram showing the operations performed todetect an anomaly. As illustrated in FIG. 5A, the anomaly detector 342may be instructed by the PM predictor 348 to provide an indicationwhether any anomalous activity is taking place on one or more secondarystorage computing devices 106 that are performing, have performed, orwill be scheduled to perform one or more secondary copy operation jobsto assist in an eventual determination as to whether a performancemetric miss will occur. Thus, the anomaly detector 342 can retrieve jobsdata for some or all of the secondary storage computing devices 106 fromthe storage manager database 146 at (1).

The anomaly detector 342 can then perform a time-series decomposition ofthe jobs data at (2). For example, the jobs data may be time-seriesdata. Once the decomposition occurs, the anomaly detector 342 cananalyze one or more components of the time-series to determine anacceptable range for succeeded jobs, an acceptable range for failedjobs, an acceptable range for killed jobs, an acceptable range forsuspended jobs, and/or an acceptable range for pending jobs at (3). Forexample, the component(s) may be error component(s). As an illustrativeexample, the time-series jobs data may include different sets oftime-series data, where one set corresponds to succeeded jobs, one setcorresponds to failed jobs, one set corresponds to killed jobs, one setcorresponds to suspended jobs, and one set corresponds to pending jobs.The anomaly detector 342 can then perform a time-series decomposition oneach set individually, and analyze the error component derived from eachindividual decomposition to determine an acceptable range for the typeof jobs associated with the respective set. Thus, the anomaly detector342 can analyze an error component derived from time-series succeededjobs data to determine an acceptable range for succeeded jobs, an errorcomponent derived from time-series failed jobs data to determine anacceptable range for failed jobs, an error component derived fromtime-series killed jobs data to determine an acceptable range for killedjobs, an error component derived from time-series suspended jobs data todetermine an acceptable range for suspended jobs, and an error componentderived from time-series pending jobs data to determine an acceptablerange for pending jobs.

Once the acceptable ranges are determined, the anomaly detector 342 candetermine whether succeeded jobs, failed jobs, killed jobs, suspendedjobs, and/or pending jobs of the secondary storage computing device(s)106 fall outside the corresponding acceptable ranges at (4). Forexample, the jobs data may include the succeeded jobs, failed jobs,killed jobs, suspended jobs, and/or pending jobs of the secondarystorage computing device(s) 106. As another example, the anomalydetector 342 can obtain the succeeded jobs, failed jobs, killed jobs,suspended jobs, and/or pending jobs of the secondary storage computingdevice(s) 106 from the individual secondary storage computing device(s)106. If any of the jobs types falls outside the corresponding range,then the anomaly detector 342 may detect an anomaly. The anomalydetector 342 can then transmit to the PM predictor 348 an indication ofwhether an anomaly is detected at (5).

FIG. 5B illustrates a block diagram showing the operations performed topredict a length of time it may take for a first secondary copyoperation job to complete. As illustrated in FIG. 5B, the job lengthpredictor 344 can retrieve machine learning training data from thestorage manager database 146 at (1).

The job length predictor 344 can then train a machine learning modelusing the machine learning training data at (2). The trained machinelearning model may predict a length of time that it may take for aparticular secondary storage computing device 106 to perform or executea secondary copy operation job.

Before, during, and/or after the job length predictor 344 trains themachine learning model, the job length predictor 344 may be instructedby the PM predictor 348 to provide an indication of a time it may taketo perform a first secondary copy operation job to assist in an eventualdetermination as to whether a performance metric miss will occur. Thus,the job length predictor 344 can retrieve machine learning input datacorresponding to the first secondary copy operation job from the storagemanager database 146 at (3). The machine learning input data may alsoinclude data associated with the secondary storage computing device 106scheduled to perform or that is performing the first secondary copyoperation job.

The job length predictor 344 can then apply the machine learning inputdata as an input to the trained machine learning model to predict alength of time it may take to perform the first secondary copy operationjob at (4). Once predicted, the job length predictor 344 can transmit tothe PM predictor 348 at (5) an indication of the predicted length oftime to complete execution of the first secondary copy operation job.

FIG. 5C illustrates a block diagram showing the operations performed topredict whether execution of a first secondary copy operation job willsucceed or fail. As illustrated in FIG. 5C, the job failure classifier346 can retrieve neural network training data from the storage managerdatabase 146 at (1).

The job failure classifier 346 can then train a neural network using theneural network training data at (2). The trained neural network maypredict whether execution of a secondary copy operation job is likely tosucceed or fail.

Before, during, and/or after the job failure classifier 346 trains theneural network, the job failure classifier 346 may be instructed by thePM predictor 348 to provide an indication of whether execution of afirst secondary copy operation job is likely to succeed or fail. Thus,the job failure classifier 346 can retrieve neural network input datacorresponding to the first secondary copy operation job and/or theinformation management system 300 that is scheduled to execute or thatis executing the first secondary copy operation job from the storagemanager database 146 at (3).

The job failure classifier 346 can apply the neural network input dataas an input to the trained neural network at (4). The job failureclassifier 346 can then determine whether the first secondary copyoperation job will fail based on an output of the trained neural network(e.g., where the output is an indication that the job will succeed orfail) at (5). Once determined, the job failure classifier 346 cantransmit to the PM predictor 348 at (6) an indication of whetherexecution of the first secondary copy operation job is predicted tofail.

While FIGS. 5A-5C are described in a particular order, this is not meantto be limiting. The operations described with respect to FIGS. 5A-5C canbe performed in any order. For example, the operations described withrespect to FIG. 5B can be performed before, during, and/or after theoperations described with respect to FIG. 5A.

FIG. 5D illustrates a block diagram showing the operations performed todetermine whether a performance metric miss will occur. As illustratedin FIG. 5D, the PM predictor 348 can determine whether a performancemetric is predicted to be missed based on information provided by theanomaly detector 342, the job length predictor 344, and/or the jobfailure classifier 346 at (1). For example, the job length predictor 344can provide an indication of a length of time it may take to execute oneor more secondary copy operation jobs and/or the job failure classifier346 may provide an indication of whether execution of one or moresecondary copy operation jobs is likely to succeed or fail. In otherwords, the operations described above with respect to FIGS. 5B and 5Ccan be repeated multiple times for different secondary copy operationjobs. The PM predictor 348 can use some or all of this information tomake the performance metric miss prediction. In fact, the PM predictor348 can use other information, alone or in combination with informationprovided by the anomaly detector 342, the job length predictor 344,and/or the job failure classifier 346, to make the prediction. Forexample, the PM predictor 348 can also analyze operation windows todetermine whether such windows may extend a time it may take to finishexecuting a secondary copy operation job as predicted by the job lengthpredictor 344. The PM predictor 348 can evaluate the provided and/ordetermined information to determine whether a sufficient number ofsecondary copy operation jobs may be completed within a threshold timeperiod defined by a performance metric. If the PM predictor 348determines that this number of secondary copy operation jobs may not becompleted within the threshold time period, then the PM predictor 348determines that a performance metric miss may occur.

If the PM predictor 348 determines that a performance metric miss mayoccur (e.g., a performance metric of the information management system300 may not be satisfied), then the PM predictor transmits to a clientcomputing device 110 or a user device (not shown) a notification at (2).The notification can be a standalone notification and/or included withina user interface rendered and/or displayed by the client computingdevice 110 or user device.

Optionally, the client computing device 110 or user device can transmitto the PM predictor 348 a request to resolve an issue identified in thenotification at (3). For example, the notification may indicate a reasonor explanation of why the performance metric may be missed. Anadministrator can use the reason or explanation to identify a potentialaction that can be taken to resolve an underlying issue preventing theperformance metric from being satisfied. As a result, the administratorcan (e.g., via a user interface) select an action to be performed on atleast one component in the information management system 300, an actionto change configuration settings, and/or the like. The client computingdevice 110 can then transmit an indication of the action to the PMpredictor 348, and the PM predictor 348 can cause the storage manager140 or another component in the information management system 300 toexecute the action.

FIG. 6 illustrates a block diagram showing the operations performed toauto-tune an information management system 300. As illustrated in FIG.6, one or more information management systems 300, such as those locatedin different geographic regions or geographic locations, can transmitmetrics relating to secondary copy operation jobs and/or systemoperations to the cell metrics data store 406 at (1). For example, themetrics may include machine learning training data and/or neural networktraining data, such as the data described herein.

Periodically and/or in response to determining that new metricsinformation has been received, the ML training system 402 can retrievesome or all of the metrics stored in the cell metrics data store 406 at(2). The ML training system 402 can then train a machine learning modelusing the metrics, where the machine learning model outputs arecommendation of a solution for resolving a performance metric miss ora potential performance metric miss at (3). Alternatively, the MLtraining system 402 can train a neural network using the metrics, wherethe neural network outputs a recommendation of a solution for resolvinga performance metric miss or a potential performance metric miss.

Before, during, and/or after the ML training system 402 trains themachine learning model (or neural network), an information managementsystem 300 (e.g., a PM predictor 348) may transmit to the PM behaviorpredictor 404 at (4) an indication that a performance metric miss hasoccurred or is predicted to occur. The indication may includeperformance metric miss data, such as one or more errors codes that ledto the prediction, a reason why a performance metric miss is detected(e.g., not enough secondary copy operation jobs are scheduled, scheduledsecondary copy operation jobs are predicted to take too long tocomplete, an operation window is preventing a secondary copy operationjob from finishing in time, etc.), the status of system resources ofcomponents in the information management system 300, and/or the like.

The PM behavior predictor 404 can then retrieved the trained machinelearning model (or trained neural network) from the ML training system402 at (5). The PM behavior predictor 404 can determine a possibleresolution to the performance metric miss by applying the performancemetric miss data as an input to the trained machine learning model (ortrained neural network) at (6). The PM behavior predictor 404 can thentransmit an indication of the possible resolution to the informationmanagement system 300 at (7). In some embodiments, the indication of thepossible resolution may be an instruction that, when received by theinformation management system 300, causes the information managementsystem 300 to execute the instruction to resolve one or more issuescausing the performance metric miss. In this way, the PM behaviorpredictor 404 may auto-tune the information management system 300.

FIG. 7 depicts some salient operations of a method 700 for predicting aperformance metric miss, according to an embodiment. The method 700 maybe implemented, for example, by a storage manager, such as the storagemanager 140. The method 700 may start at block 702.

At block 702, data associated with a first secondary copy operation jobto be performed by a secondary storage computing device is retrieved.The data may include machine learning input data associated with thefirst secondary copy operation job and/or a secondary storage computingdevice 106 scheduled to perform or that is performing the firstsecondary copy operation job.

At block 704, the retrieved data is applied as an input to a machinelearning model trained to predict a length of time to complete asecondary copy operation job. For example, the machine learning modelmay have been trained at a previous time based on machine learningtraining data stored in the storage manager database 146.

Alternatively, the retrieved data may be applied as an input a neuralnetwork trained to predict a length of time to complete a secondary copyoperation job. The neural network can be trained prior to a performancemetric miss determination being made.

At block 706, whether an operation window is scheduled during the lengthof time to complete the first secondary copy operation job predicted bythe machine learning model is determined. For example, an operationwindow can include a blackout window, a maintenance window, and/or thelike.

If the storage manager 140 determines that an operation window isscheduled to start at a time during which execution of the firstsecondary copy operation job is ongoing, then the storage manager 140may determine that the first secondary copy operation job may be pausedduring the operation window and resume execution after the operationwindow expires. As a result, the storage manager 140 may determine thatthe first secondary copy operation job is scheduled to be finished atthe time predicted by the machine learning model plus a time duration ofthe operation window.

At block 708, whether a performance metric miss may occur is determinedbased on whether an operation window is scheduled and the predictedlength of time to complete the first secondary copy operation job. Forexample, the storage manager 140 may determine that a performance metricmiss may occur if the predicted length of time plus the time duration ofthe operation window results in a time that occurs after a thresholdtime period defined by the performance metric.

Alternatively or in addition, the storage manager 140 may consider otherinformation before determining that a performance metric miss may occur.For example, the storage manager 140 may determine whether anomalousbehavior associated with one or more secondary storage computing devices106 is present, whether execution of any secondary copy operation jobsare predicted to fail, whether any secondary copy operation jobs havebeen pending for a long period of time, and/or the like.

At block 710, a notification is generated in response to a determinationthat the performance metric miss may occur. The notification can betransmitted to a client computing device 110 or other user device. Thenotification can also be included in a user interface rendered and/ordisplayed by a client computing device 110 or other user device.Optionally, the notification, when opened or selected, may indicate areason why a performance metric miss is detected. After the notificationis generated, the method 700 is complete.

FIG. 8 depicts some salient operations of another method 800 forpredicting a performance metric miss, according to an embodiment. Themethod 800 may be implemented, for example, by a storage manager, suchas the storage manager 140. The method 800 may start at block 802.

At block 802, neural network input data associated with a firstinformation management system and a first secondary copy operation jobis retrieved. For example, the neural network input data may be storedin the storage manager database 146. The neural network input data canbe retrieved in response to the PM predictor 348 requesting informationindicating whether the first secondary copy operation job is likely tosucceed or fail.

At block 804, the neural network input data is applied as an input to aneural network trained to output an indication of whether execution ofthe first secondary copy operation job will fail. The neural network maybe trained before the PM predictor 348 requests information indicatingwhether execution of the first secondary copy operation job is likely tosucceed or fail or after the PM predictor 348 requests this information.

At block 806, a determination is made as to whether any pending orscheduled secondary copy operation job will satisfy a performance metricin response to the neural network outputting a prediction that executionof the first secondary copy operation job will fail. For example, theneural network may predict that execution of the first secondary copyoperation job may fail. This does not necessarily mean that aperformance metric miss may occur, however. The performance metric maydefine a number of secondary copy operation jobs that are to becompleted within a threshold time period. If this number of secondarycopy operation jobs can be completed within the threshold time perioddespite execution of one or more other secondary copy operation jobsfailing, then the performance metric may still be satisfied. Thus, theneural network outputting that execution of the first secondary copyoperation job is predicted to fail may not automatically mean that aperformance metric miss will occur.

At block 808, a notification is generated in response to a determinationthat execution of the first secondary copy operation job is expected tofail and the completion of other secondary copy operation jobs will notbe sufficient to satisfy the performance metric. The notification can betransmitted to a client computing device 110 or other user device. Thenotification can also be included in a user interface rendered and/ordisplayed by a client computing device 110 or other user device.Optionally, the notification, when opened or selected, may indicate areason why a performance metric miss is detected. After the notificationis generated, the method 800 is complete.

FIG. 9 depicts some salient operations of a method 900 for auto-tuningor automatically configuring an information management system, accordingto an embodiment. The method 900 may be implemented, for example, by acloud-based performance metric failure resolver, such as the cloud-basedperformance metric failure resolver 400. The method 900 may start atblock 902.

At block 902, metrics are retrieved from a plurality of informationmanagement systems. For example, the metrics may include machinelearning input data, machine learning training data, neural networkinput data, and/or neural network training data stored in the individualinformation management systems. The metrics can be stored locally.

At block 904, periodically, in response to receiving metrics, and/or inresponse to an information management system indicating that aperformance metric miss is occurring or may occur, a machine learningmodel is trained to identify a possible resolution to a performancemetric miss using the retrieved metrics. The machine learning model canbe a generic machine learning model applicable to multiple informationmanagement systems or a specific machine learning model applicable to asingle information management system (and where the specific machinelearning model may be trained using metrics from any informationmanagement system).

Alternatively, a neural network is trained using the retrieved metrics.The neural network can be a generic neural network applicable tomultiple information management systems or a specific neural networkapplicable to a single information management system (and where thespecific neural network may be trained using metrics from anyinformation management system).

At block 906, performance metric miss data is obtained from a firstinformation management system in the plurality of information managementsystems. For example, the performance metric miss data may indicate aperformance metric miss predicted to occur or that has already occurredin the first information management system. The prediction may be madeby the PM predictor 348 in the first information management system.

At block 908, the performance metric miss data is applied as an input tothe trained machine learning model. As a result, the trained machinelearning model may output a possible resolution to the performancemetric miss predicted to occur or that has occurred.

At block 910, an action to be performed is determined based on an outputof the trained machine learning model. For example, the action may beauto-tuning one or more parameters of the information management system,configuring or re-configuring one or more settings of the informationmanagement system, causing the information management system to scheduleadditional secondary copy operation jobs prior to a performancemetric-defined threshold time period expiring, and/or the like.

At block 912, the first information management system is caused toperform the determined action. For example, the cloud-based performancemetric failure resolver 400 can instruct the first informationmanagement system to perform the determined action. Alternatively, thecloud-based performance metric failure resolver 400 can transmit anotification to the first information management system suggesting thatthe determined action be performed, and an administrator can decidewhether or not to execute the determined action. After the firstinformation management system is caused to perform the determinedaction, the method 900 is complete.

FIG. 10 depicts a graphical user interface 1000 showing a performancemetric miss notification or alert, according to an embodiment. Thegraphical user interface 1000 can be generated by the storage manager140 or another component in the system 300.

As illustrated in FIG. 10, the user interface 1000 can indicate one ormore clients (e.g., information management systems 300) that may beexperiencing a performance metric miss. For example, notification 1002in the user interface 1000 may indicate a client that may beexperiencing a performance metric miss and a reason why the performancemetric miss is predicted. Here, the performance metric miss is predictedbecause an operation window overlaps with a time during which asecondary copy operation job is to be executed, thereby delayingexecution of the secondary copy operation job by 3 hours. As a result,the secondary copy operation job may finish executing after thethreshold time period defined by the performance metric. Thenotification 1002 can indicate what action to take (e.g., start andcomplete a secondary copy operation job prior to a particular time, suchas Jul. 31, 2020 at 4:29 pm) in order to avoid a performance metricmiss. Optionally, the information management system 300 can beauto-configured or auto-tuned to perform the action described in thenotification 1002.

Notification 1004 in the user interface 1000 may also indicate a clientthat may be experiencing a performance metric miss and a reason why theperformance metric miss is predicted. Here, the performance metric missis predicted because one or more secondary copy operation jobs arerunning beyond a runtime threshold (e.g., the jobs are pending forlonger than a threshold time) and it is unclear whether such jobs willcomplete by the time the threshold time duration defined by theperformance metric expires. The notification 1004 can indicate whataction to take (e.g., start and complete a secondary copy operation jobprior to a particular time, such as Jul. 31, 2020 at 4:59 pm) in orderto avoid a performance metric miss. Optionally, the informationmanagement system 300 can be auto-configured or auto-tuned to performthe action described in the notification 1004.

Notification 1006 in the user interface 1000 may also indicate a clientthat may be experiencing a performance metric miss and a reason why theperformance metric miss is predicted. Here, the performance metric missis predicted because there is a history of secondary copy operation jobsfailing in the client (e.g., as determined based on the anomaly detector342 or the job failure classifier 346). The notification 1006 canindicate what action to take (e.g., start and complete a secondary copyoperation job prior to a particular time, such as Jul. 31, 2020 at 1:59pm) in order to avoid a performance metric miss. Optionally, theinformation management system 300 can be auto-configured or auto-tunedto perform the action described in the notification 1006.

Notification 1008 in the user interface 1000 may also indicate a clientthat may be experiencing a performance metric miss and a reason why theperformance metric miss is predicted. Here, the performance metric missis predicted because the client does not have any backup jobs or othersecondary copy operation jobs scheduled to run before the threshold timeperiod defined by the performance metric expires. The notification 1006can indicate what action to take (e.g., start and complete a secondarycopy operation job prior to a particular time, such as Jul. 31, 2020 at11:59 am) in order to avoid a performance metric miss. Optionally, theinformation management system 300 can be auto-configured or auto-tunedto perform the action described in the notification 1008.

In regard to the figures described herein, other embodiments arepossible, such that the above-recited components, steps, blocks,operations, and/or messages/requests/queries/instructions aredifferently arranged, sequenced, sub-divided, organized, and/orcombined. In some embodiments, a different component may initiate orexecute a given operation. For example, in some embodiments, the PMpredictor 348 can perform any of the functionality described herein asbeing performed by the anomaly detector 342, the job length predictor344, and/or the job failure classifier 346. As another example, acomponent separate from the storage manager 140 residing internal to orexternal to the information management system 300 can perform any of thefunctionality described herein as being performed by the anomalydetector 342, the job length predictor 344, the job failure classifier346, and/or the PM predictor 348.

Example Embodiments

Some example enumerated embodiments are recited in this section in theform of methods, systems, and non-transitory computer-readable media,without limitation.

One aspect of the disclosure provides a networked information managementsystem comprising a secondary storage computing device. The networkedinformation management system further comprises a second computingdevice in communication with the secondary storage computing device,where the second computing device is configured with computer-executableinstructions that, when executed, cause the second computing device to:obtain data associated with a first secondary copy operation job to beperformed by the secondary storage computing device; apply the obtaineddata as an input to a trained machine learning model, where applicationof the obtained data as an input to the trained machine learning modelcauses the trained machine learning model to output a prediction of alength of time to complete execution of the first secondary copyoperation job; determine that an operation window is scheduled duringthe length of time to complete execution of the first secondary copyoperation job; determine that a performance metric associated with thenetworked information management system will not be satisfied inresponse to the determination that the operation window is scheduledduring the length of the time to complete execution of the firstsecondary copy operation job; and generate a notification in response tothe determination that the performance metric will not be satisfied.

The networked information management system of the preceding paragraphcan include any sub-combination of the following features: wherecomputer-executable instructions, when executed, cause the secondcomputing device to train the machine learning model using machinelearning training data associated with the networked informationmanagement system; where the machine learning training data comprises atleast one of a time taken to perform a second secondary copy operation,a number of secondary copy operation jobs performed by the secondarystorage computing device that have failed within a threshold timeperiod, a number of consecutive secondary copy operation jobs performedby the secondary storage computing device that have failed; or a size ofdata upon which the second secondary copy operation job is performed;where computer-executable instructions, when executed, cause the secondcomputing device to apply at least some of the obtained data as an inputto a trained neural network, where application of the obtained data asan input to the trained neural network causes the trained neural networkto output an indication of whether execution of a second secondary copyoperation job is likely to succeed or fail; where computer-executableinstructions, when executed, cause the second computing device todetermine that the performance metric will not be satisfied in responseto the determination that the operation window is scheduled during thelength of the time to complete execution of the first secondary copyoperation job and in response to a determination that execution of thesecond secondary copy operation job is likely to fail; wherecomputer-executable instructions, when executed, cause the secondcomputing device to determine that the performance metric will besatisfied in response to the determination that the operation window isscheduled during the length of the time to complete execution of thefirst secondary copy operation job and in response to a determinationthat execution of the second secondary copy operation job is likely tosucceed; where the performance metric defines a number of secondary copyoperation jobs to complete within a threshold time period; wherecomputer-executable instructions, when executed, cause the secondcomputing device to determine that the operation window being scheduledduring the length of time to complete execution of the first secondarycopy operation job will delay the first secondary copy operation jobfrom being completed until after the threshold time period expires; andwhere computer-executable instructions, when executed, cause the secondcomputing device to cause the generated notification to appear in a userinterface displayed on a client computing device, where the notificationindicates an explanation of why the performance metric is determined tonot be satisfied.

Another aspect of the disclosure provides a computer-implemented methodcomprising: obtaining data associated with a first secondary copyoperation job to be performed by a secondary storage computing device ina networked information management system; applying the obtained data asan input to a trained machine learning model, where application of theobtained data as an input to the trained machine learning model causesthe trained machine learning model to output a prediction of a length oftime to complete execution of the first secondary copy operation job;determining that an operation window is scheduled during the length oftime to complete execution of the first secondary copy operation job;determining that a performance metric associated with the networkedinformation management system will not be satisfied in response to thedetermination that the operation window is scheduled during the lengthof the time to complete execution of the first secondary copy operationjob; and generating a notification in response to the determination thatthe performance metric will not be satisfied.

The computer-implemented method of the preceding paragraph can includeany sub-combination of the following features: where thecomputer-implemented method further comprises training the machinelearning model using machine learning training data associated with thenetworked information management system; where the machine learningtraining data comprises at least one of a time taken to perform a secondsecondary copy operation, a number of secondary copy operation jobsperformed by the secondary storage computing device that have failedwithin a threshold time period, a number of consecutive secondary copyoperation jobs performed by the secondary storage computing device thathave failed; or a size of data upon which the second secondary copyoperation job is performed; where the computer-implemented methodfurther comprises applying at least some of the obtained data as aninput to a trained neural network, where application of the obtaineddata as an input to the trained neural network causes the trained neuralnetwork to output an indication of whether execution of a secondsecondary copy operation job is likely to succeed or fail; wheredetermining that a performance metric will not be satisfied furthercomprises determining that the performance metric will not be satisfiedin response to the determination that the operation window is scheduledduring the length of the time to complete execution of the firstsecondary copy operation job and in response to a determination thatexecution of the second secondary copy operation job is likely to fail;where the computer-implemented method further comprises determining thatthe performance metric will be satisfied in response to thedetermination that the operation window is scheduled during the lengthof the time to complete execution of the first secondary copy operationjob and in response to a determination that execution of the secondsecondary copy operation job is likely to succeed; where the performancemetric defines a number of secondary copy operation jobs to completewithin a threshold time period; where determining that an operationwindow is scheduled during the length of time to complete execution ofthe first secondary copy operation job further comprises determiningthat the operation window being scheduled during the length of time tocomplete execution of the first secondary copy operation job will delaythe first secondary copy operation job from being completed until afterthe threshold time period expires; and where the computer-implementedmethod further comprises causing the generated notification to appear ina user interface displayed on a client computing device, where thenotification indicates an explanation of why the performance metric isdetermined to not be satisfied.

Another aspect of the disclosure provides a non-transitory computerreadable medium storing computer-executable instructions that, whenexecuted by a networked information management system, cause thenetworked information management system to: obtain data associated witha first secondary copy operation job to be performed by a secondarystorage computing device in the networked information management system;apply the obtained data as an input to a trained machine learning model,where application of the obtained data as an input to the trainedmachine learning model causes the trained machine learning model tooutput a prediction of a length of time to complete execution of thefirst secondary copy operation job; determine that an operation windowis scheduled during the length of time to complete execution of thefirst secondary copy operation job; determine that a performance metricassociated with the networked information management system will not besatisfied in response to the determination that the operation window isscheduled during the length of the time to complete execution of thefirst secondary copy operation job; and generate a notification inresponse to the determination that the performance metric will not besatisfied.

The non-transitory computer readable medium of the preceding paragraphcan include any sub-combination of the following features: where thecomputer-executable instructions, when executed, further cause thenetworked information management system to: apply at least some of theobtained data as an input to a trained neural network, where applicationof the obtained data as an input to the trained neural network causesthe trained neural network to output an indication of whether executionof a second secondary copy operation job is likely to succeed or fail,and determine that the performance metric will not be satisfied inresponse to the determination that the operation window is scheduledduring the length of the time to complete execution of the firstsecondary copy operation job and in response to a determination thatexecution of the second secondary copy operation job is likely to fail.

In other embodiments, a system or systems may operate according to oneor more of the methods and/or computer-readable media recited in thepreceding paragraphs. In yet other embodiments, a method or methods mayoperate according to one or more of the systems and/or computer-readablemedia recited in the preceding paragraphs. In yet more embodiments, acomputer-readable medium or media, excluding transitory propagatingsignals, may cause one or more computing devices having one or moreprocessors and non-transitory computer-readable memory to operateaccording to one or more of the systems and/or methods recited in thepreceding paragraphs.

Terminology

Conditional language, such as, among others, “can,” “could,” “might,” or“may,” unless specifically stated otherwise, or otherwise understoodwithin the context as used, is generally intended to convey that certainembodiments include, while other embodiments do not include, certainfeatures, elements and/or steps. Thus, such conditional language is notgenerally intended to imply that features, elements and/or steps are inany way required for one or more embodiments or that one or moreembodiments necessarily include logic for deciding, with or without userinput or prompting, whether these features, elements and/or steps areincluded or are to be performed in any particular embodiment.

Unless the context clearly requires otherwise, throughout thedescription and the claims, the words “comprise,” “comprising,” and thelike are to be construed in an inclusive sense, as opposed to anexclusive or exhaustive sense, i.e., in the sense of “including, but notlimited to.” As used herein, the terms “connected,” “coupled,” or anyvariant thereof means any connection or coupling, either direct orindirect, between two or more elements; the coupling or connectionbetween the elements can be physical, logical, or a combination thereof.Additionally, the words “herein,” “above,” “below,” and words of similarimport, when used in this application, refer to this application as awhole and not to any particular portions of this application. Where thecontext permits, words using the singular or plural number may alsoinclude the plural or singular number respectively. The word “or” inreference to a list of two or more items, covers all of the followinginterpretations of the word: any one of the items in the list, all ofthe items in the list, and any combination of the items in the list.Likewise the term “and/or” in reference to a list of two or more items,covers all of the following interpretations of the word: any one of theitems in the list, all of the items in the list, and any combination ofthe items in the list.

In some embodiments, certain operations, acts, events, or functions ofany of the algorithms described herein can be performed in a differentsequence, can be added, merged, or left out altogether (e.g., not allare necessary for the practice of the algorithms). In certainembodiments, operations, acts, functions, or events can be performedconcurrently, e.g., through multi-threaded processing, interruptprocessing, or multiple processors or processor cores or on otherparallel architectures, rather than sequentially.

Systems and modules described herein may comprise software, firmware,hardware, or any combination(s) of software, firmware, or hardwaresuitable for the purposes described. Software and other modules mayreside and execute on servers, workstations, personal computers,computerized tablets, PDAs, and other computing devices suitable for thepurposes described herein. Software and other modules may be accessiblevia local computer memory, via a network, via a browser, or via othermeans suitable for the purposes described herein. Data structuresdescribed herein may comprise computer files, variables, programmingarrays, programming structures, or any electronic information storageschemes or methods, or any combinations thereof, suitable for thepurposes described herein. User interface elements described herein maycomprise elements from graphical user interfaces, interactive voiceresponse, command line interfaces, and other suitable interfaces.

Further, processing of the various components of the illustrated systemscan be distributed across multiple machines, networks, and othercomputing resources. Two or more components of a system can be combinedinto fewer components. Various components of the illustrated systems canbe implemented in one or more virtual machines, rather than in dedicatedcomputer hardware systems and/or computing devices. Likewise, the datarepositories shown can represent physical and/or logical data storage,including, e.g., storage area networks or other distributed storagesystems. Moreover, in some embodiments the connections between thecomponents shown represent possible paths of data flow, rather thanactual connections between hardware. While some examples of possibleconnections are shown, any of the subset of the components shown cancommunicate with any other subset of components in variousimplementations.

Embodiments are also described above with reference to flow chartillustrations and/or block diagrams of methods, apparatus (systems) andcomputer program products. Each block of the flow chart illustrationsand/or block diagrams, and combinations of blocks in the flow chartillustrations and/or block diagrams, may be implemented by computerprogram instructions. Such instructions may be provided to a processorof a general purpose computer, special purpose computer,specially-equipped computer (e.g., comprising a high-performancedatabase server, a graphics subsystem, etc.) or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor(s) of the computer or other programmabledata processing apparatus, create means for implementing the actsspecified in the flow chart and/or block diagram block or blocks. Thesecomputer program instructions may also be stored in a non-transitorycomputer-readable memory that can direct a computer or otherprogrammable data processing apparatus to operate in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including instruction meanswhich implement the acts specified in the flow chart and/or blockdiagram block or blocks. The computer program instructions may also beloaded to a computing device or other programmable data processingapparatus to cause operations to be performed on the computing device orother programmable apparatus to produce a computer implemented processsuch that the instructions which execute on the computing device orother programmable apparatus provide steps for implementing the actsspecified in the flow chart and/or block diagram block or blocks.

Any patents and applications and other references noted above, includingany that may be listed in accompanying filing papers, are incorporatedherein by reference. Aspects of one or more embodiments can be modified,if necessary, to employ the systems, functions, and concepts of thevarious references described above. These and other changes can be madein light of the above Detailed Description. While the above descriptiondescribes certain examples, and describes the best mode contemplated, nomatter how detailed the above appears in text, different embodiments canbe practiced in many ways. Details of the system may vary considerablyin its specific implementation. As noted above, particular terminologyused when describing certain features should not be taken to imply thatthe terminology is being redefined herein to be restricted to anyspecific characteristics, features with which that terminology isassociated. In general, the terms used in the following claims shouldnot be construed to limit the scope the specific examples disclosed inthe specification, unless the above Detailed Description sectionexplicitly defines such terms. Accordingly, the actual scope encompassesnot only the disclosed examples, but also all equivalent ways ofpracticing or implementing the claims.

To reduce the number of claims, certain aspects are presented below incertain claim forms, but the applicant contemplates other aspects in anynumber of claim forms. For example, while only one aspect may be recitedas a means-plus-function claim under 35 U.S.C sec. 112(f) (AIA), otheraspects may likewise be embodied as a means-plus-function claim, or inother forms, such as being embodied in a computer-readable medium. Anyclaims intended to be treated under 35 U.S.C. § 112(f) will begin withthe words “means for,” but use of the term “for” in any other context isnot intended to invoke treatment under 35 U.S.C. § 112(f). Accordingly,the applicant reserves the right to pursue additional claims afterfiling this application, in either this application or in a continuingapplication.

What is claimed is:
 1. A networked information management systemcomprising: a secondary storage computing device; and a second computingdevice in communication with the secondary storage computing device,wherein the second computing device is configured withcomputer-executable instructions that, when executed, cause the secondcomputing device to: obtain data associated with a first secondary copyoperation job to be performed by the secondary storage computing device;apply the obtained data as an input to a trained machine learning model,wherein application of the obtained data as an input to the trainedmachine learning model causes the trained machine learning model tooutput a prediction of a length of time to complete execution of thefirst secondary copy operation job; determine that an operation windowis scheduled during the length of time to complete execution of thefirst secondary copy operation job; determine that a performance metricassociated with the networked information management system will not besatisfied in response to the determination that the operation window isscheduled during the length of the time to complete execution of thefirst secondary copy operation job; and generate a notification inresponse to the determination that the performance metric will not besatisfied.
 2. The networked information management system of claim 1,wherein computer-executable instructions, when executed, cause thesecond computing device to train the machine learning model usingmachine learning training data associated with the networked informationmanagement system.
 3. The networked information management system ofclaim 2, wherein the machine learning training data comprises at leastone of a time taken to perform a second secondary copy operation, anumber of secondary copy operation jobs performed by the secondarystorage computing device that have failed within a threshold timeperiod, a number of consecutive secondary copy operation jobs performedby the secondary storage computing device that have failed; or a size ofdata upon which the second secondary copy operation job is performed. 4.The networked information management system of claim 1, whereincomputer-executable instructions, when executed, cause the secondcomputing device to apply at least some of the obtained data as an inputto a trained neural network, wherein application of the obtained data asan input to the trained neural network causes the trained neural networkto output an indication of whether execution of a second secondary copyoperation job is likely to succeed or fail.
 5. The networked informationmanagement system of claim 4, wherein computer-executable instructions,when executed, cause the second computing device to determine that theperformance metric will not be satisfied in response to thedetermination that the operation window is scheduled during the lengthof the time to complete execution of the first secondary copy operationjob and in response to a determination that execution of the secondsecondary copy operation job is likely to fail.
 6. The networkedinformation management system of claim 4, wherein computer-executableinstructions, when executed, cause the second computing device todetermine that the performance metric will be satisfied in response tothe determination that the operation window is scheduled during thelength of the time to complete execution of the first secondary copyoperation job and in response to a determination that execution of thesecond secondary copy operation job is likely to succeed.
 7. Thenetworked information management system of claim 1, wherein theperformance metric defines a number of secondary copy operation jobs tocomplete within a threshold time period.
 8. The networked informationmanagement system of claim 7, wherein computer-executable instructions,when executed, cause the second computing device to determine that theoperation window being scheduled during the length of time to completeexecution of the first secondary copy operation job will delay the firstsecondary copy operation job from being completed until after thethreshold time period expires.
 9. The networked information managementsystem of claim 1, wherein computer-executable instructions, whenexecuted, cause the second computing device to cause the generatednotification to appear in a user interface displayed on a clientcomputing device, wherein the notification indicates an explanation ofwhy the performance metric is determined to not be satisfied.
 10. Acomputer-implemented method comprising: obtaining data associated with afirst secondary copy operation job to be performed by a secondarystorage computing device in a networked information management system;applying the obtained data as an input to a trained machine learningmodel, wherein application of the obtained data as an input to thetrained machine learning model causes the trained machine learning modelto output a prediction of a length of time to complete execution of thefirst secondary copy operation job; determining that an operation windowis scheduled during the length of time to complete execution of thefirst secondary copy operation job; determining that a performancemetric associated with the networked information management system willnot be satisfied in response to the determination that the operationwindow is scheduled during the length of the time to complete executionof the first secondary copy operation job; and generating a notificationin response to the determination that the performance metric will not besatisfied.
 11. The computer-implemented method of claim 10, furthercomprising training the machine learning model using machine learningtraining data associated with the networked information managementsystem.
 12. The computer-implemented method of claim 11, wherein themachine learning training data comprises at least one of a time taken toperform a second secondary copy operation, a number of secondary copyoperation jobs performed by the secondary storage computing device thathave failed within a threshold time period, a number of consecutivesecondary copy operation jobs performed by the secondary storagecomputing device that have failed; or a size of data upon which thesecond secondary copy operation job is performed.
 13. Thecomputer-implemented method of claim 10, further comprising applying atleast some of the obtained data as an input to a trained neural network,wherein application of the obtained data as an input to the trainedneural network causes the trained neural network to output an indicationof whether execution of a second secondary copy operation job is likelyto succeed or fail.
 14. The computer-implemented method of claim 13,wherein determining that a performance metric will not be satisfiedfurther comprises determining that the performance metric will not besatisfied in response to the determination that the operation window isscheduled during the length of the time to complete execution of thefirst secondary copy operation job and in response to a determinationthat execution of the second secondary copy operation job is likely tofail.
 15. The computer-implemented method of claim 13, furthercomprising determining that the performance metric will be satisfied inresponse to the determination that the operation window is scheduledduring the length of the time to complete execution of the firstsecondary copy operation job and in response to a determination thatexecution of the second secondary copy operation job is likely tosucceed.
 16. The computer-implemented method of claim 10, wherein theperformance metric defines a number of secondary copy operation jobs tocomplete within a threshold time period.
 17. The computer-implementedmethod of claim 16, wherein determining that an operation window isscheduled during the length of time to complete execution of the firstsecondary copy operation job further comprises determining that theoperation window being scheduled during the length of time to completeexecution of the first secondary copy operation job will delay the firstsecondary copy operation job from being completed until after thethreshold time period expires.
 18. The computer-implemented method ofclaim 10, further comprising causing the generated notification toappear in a user interface displayed on a client computing device,wherein the notification indicates an explanation of why the performancemetric is determined to not be satisfied.
 19. A non-transitory computerreadable medium storing computer-executable instructions that, whenexecuted by a networked information management system, cause thenetworked information management system to: obtain data associated witha first secondary copy operation job to be performed by a secondarystorage computing device in the networked information management system;apply the obtained data as an input to a trained machine learning model,wherein application of the obtained data as an input to the trainedmachine learning model causes the trained machine learning model tooutput a prediction of a length of time to complete execution of thefirst secondary copy operation job; determine that an operation windowis scheduled during the length of time to complete execution of thefirst secondary copy operation job; determine that a performance metricassociated with the networked information management system will not besatisfied in response to the determination that the operation window isscheduled during the length of the time to complete execution of thefirst secondary copy operation job; and generate a notification inresponse to the determination that the performance metric will not besatisfied.
 20. The non-transitory computer readable medium of claim 19,wherein the computer-executable instructions, when executed, furthercause the networked information management system to: apply at leastsome of the obtained data as an input to a trained neural network,wherein application of the obtained data as an input to the trainedneural network causes the trained neural network to output an indicationof whether execution of a second secondary copy operation job is likelyto succeed or fail; and determine that the performance metric will notbe satisfied in response to the determination that the operation windowis scheduled during the length of the time to complete execution of thefirst secondary copy operation job and in response to a determinationthat execution of the second secondary copy operation job is likely tofail.